diff --git a/R/score_model_out.R b/R/score_model_out.R
index 0769ed0..4d3c781 100644
--- a/R/score_model_out.R
+++ b/R/score_model_out.R
@@ -3,8 +3,8 @@
 #' Scores model outputs with a single `output_type` against observed data.
 #'
 #' @param model_out_tbl Model output tibble with predictions
-#' @param target_observations Observed 'ground truth' data to be compared to
-#' predictions
+#' @param oracle_output Predictions that would have been generated by an oracle
+#' model that knew the observed target data values in advance
 #' @param metrics Character vector of scoring metrics to compute. If `NULL`
 #' (the default), appropriate metrics are chosen automatically. See details
 #' for more.
@@ -18,6 +18,9 @@
 #' all other output types, this is ignored.
 #'
 #' @details
+#' See the hubverse documentation for the expected format of the
+#' [oracle output data](https://hubverse.io/en/latest/user-guide/target-data.html#oracle-output).
+#'
 #' Default metrics are provided by the `scoringutils` package. You can select
 #' metrics by passing in a character vector of metric names to the `metrics`
 #' argument.
@@ -58,7 +61,7 @@
 #' quantile_scores <- score_model_out(
 #'   model_out_tbl = hubExamples::forecast_outputs |>
 #'     dplyr::filter(.data[["output_type"]] == "quantile"),
-#'   target_observations = hubExamples::forecast_target_observations,
+#'   oracle_output = hubExamples::forecast_oracle_output,
 #'   metrics = c("wis", "interval_coverage_80", "interval_coverage_90"),
 #'   by = "model_id"
 #' )
@@ -72,7 +75,7 @@
 #' pmf_scores <- score_model_out(
 #'   model_out_tbl = hubExamples::forecast_outputs |>
 #'     dplyr::filter(.data[["output_type"]] == "pmf"),
-#'   target_observations = hubExamples::forecast_target_observations,
+#'   oracle_output = hubExamples::forecast_oracle_output,
 #'   metrics = "log_score",
 #'   by = c("model_id", "location", "horizon")
 #' )
@@ -85,7 +88,7 @@
 #' American Statistical Association 106 (494): 746–62. <doi: 10.1198/jasa.2011.r10138>.
 #'
 #' @export
-score_model_out <- function(model_out_tbl, target_observations, metrics = NULL,
+score_model_out <- function(model_out_tbl, oracle_output, metrics = NULL,
                             summarize = TRUE, by = "model_id",
                             output_type_id_order = NULL) {
   # check that model_out_tbl has a single output_type that is supported by this package
@@ -94,10 +97,10 @@ score_model_out <- function(model_out_tbl, target_observations, metrics = NULL,
 
   # assemble data for scoringutils
   su_data <- switch(output_type,
-    quantile = transform_quantile_model_out(model_out_tbl, target_observations),
-    pmf = transform_pmf_model_out(model_out_tbl, target_observations, output_type_id_order),
-    mean = transform_point_model_out(model_out_tbl, target_observations, output_type),
-    median = transform_point_model_out(model_out_tbl, target_observations, output_type),
+    quantile = transform_quantile_model_out(model_out_tbl, oracle_output),
+    pmf = transform_pmf_model_out(model_out_tbl, oracle_output, output_type_id_order),
+    mean = transform_point_model_out(model_out_tbl, oracle_output, output_type),
+    median = transform_point_model_out(model_out_tbl, oracle_output, output_type),
     NULL # default, should not happen because of the validation above
   )
 
@@ -122,7 +125,7 @@ score_model_out <- function(model_out_tbl, target_observations, metrics = NULL,
 #' Get scoring metrics
 #'
 #' @param forecast A scoringutils `forecast` object (see
-#' [scoringutils::as_forecast()] for details).
+#' [scoringutils::as_forecast_doc_template()] for details).
 #' @inheritParams score_model_out
 #'
 #' @return a list of metric functions as required by scoringutils::score()
diff --git a/R/transform_pmf_model_out.R b/R/transform_pmf_model_out.R
index 25c98cf..a0698a2 100644
--- a/R/transform_pmf_model_out.R
+++ b/R/transform_pmf_model_out.R
@@ -1,22 +1,23 @@
 #' Transform pmf model output into a forecast object
 #'
 #' @param model_out_tbl Model output tibble with predictions
-#' @param target_observations Observed 'ground truth' data to be compared against predictions
+#' @param oracle_output Predictions that would have been generated by an oracle
+#' model that knew the observed target data values in advance
 #' @param output_type_id_order For nominal variables, this should be `NULL` (the default).
 #' For ordinal variables, this is a vector of levels for pmf forecasts, in
 #' increasing order of the levels.
 #'
 #' @return forecast_quantile
 #' @importFrom rlang .data
-transform_pmf_model_out <- function(model_out_tbl, target_observations, output_type_id_order = NULL) {
-  model_out_tbl <- validate_model_out_target_obs(model_out_tbl, target_observations)
+transform_pmf_model_out <- function(model_out_tbl, oracle_output, output_type_id_order = NULL) {
+  model_out_tbl <- validate_model_oracle_out(model_out_tbl, oracle_output)
 
-  # subset both model_out_tbl and target_observations to output_type == "pmf"
+  # subset both model_out_tbl and oracle_output to output_type == "pmf"
   model_out_tbl <- model_out_tbl |>
     dplyr::filter(.data[["output_type"]] == "pmf")
 
-  if (c("output_type") %in% colnames(target_observations)) {
-    target_observations <- target_observations |>
+  if (c("output_type") %in% colnames(oracle_output)) {
+    oracle_output <- oracle_output |>
       dplyr::filter(.data[["output_type"]] == "pmf") |>
       dplyr::select(-c("output_type"))
   }
@@ -24,7 +25,7 @@ transform_pmf_model_out <- function(model_out_tbl, target_observations, output_t
   # validate or set output_type_id_order
   if (!is.null(output_type_id_order)) {
     cli::cli_abort(
-      "ordinal variables are not yet supported (we expect that they will be by the time we release this package)."
+      "ordinal variables are not yet supported."
     )
     is_ordinal <- TRUE
   } else {
@@ -39,17 +40,18 @@ transform_pmf_model_out <- function(model_out_tbl, target_observations, output_t
     dplyr::rename(model = "model_id")
 
   data <- dplyr::left_join(
-    model_out_tbl, target_observations,
-    by = c(task_id_cols[task_id_cols %in% colnames(target_observations)], "output_type_id"),
+    model_out_tbl, oracle_output,
+    by = c(task_id_cols[task_id_cols %in% colnames(oracle_output)], "output_type_id"),
     relationship = "many-to-one"
   ) |>
     dplyr::group_by(dplyr::across(dplyr::all_of(c(task_id_cols, "model")))) |>
     dplyr::mutate(
-      observation = .data[["output_type_id"]][.data[["observation"]] == 1],
+      observation = .data[["output_type_id"]][.data[["oracle_value"]] == 1],
       observation = factor(.data[["observation"]], levels = output_type_id_order, ordered = is_ordinal),
       output_type_id = factor(.data[["output_type_id"]], levels = output_type_id_order, ordered = is_ordinal)
     ) |>
-    dplyr::ungroup()
+    dplyr::ungroup() |>
+    dplyr::select(-tidyselect::all_of("oracle_value"))
 
   forecast_pmf <- scoringutils::as_forecast_nominal(
     data,
diff --git a/R/transform_point_model_out.R b/R/transform_point_model_out.R
index e49f46f..fb7fd99 100644
--- a/R/transform_point_model_out.R
+++ b/R/transform_point_model_out.R
@@ -1,8 +1,8 @@
 #' Transform either mean or median model output into a point forecast object:
 #'
-#'
 #' @param model_out_tbl Model output tibble with predictions
-#' @param target_observations Observed 'ground truth' data to be compared against predictions
+#' @param oracle_output Predictions that would have been generated by an oracle
+#' model that knew the observed target data values in advance
 #' @param output_type Forecast output type: "mean" or "median"
 #'
 #' @return forecast_point
@@ -10,14 +10,14 @@
 #' @details This function transforms a model output tibble in the Hubverse
 #' format (with either "mean" or "median" output type) to a scoringutils "point"
 #' forecast object
-transform_point_model_out <- function(model_out_tbl, target_observations, output_type) {
+transform_point_model_out <- function(model_out_tbl, oracle_output, output_type) {
   if ((!inherits(output_type, "character")) || (!output_type %in% c("mean", "median"))) {
     cli::cli_abort(
       "invalid 'output_type': {.val {output_type}} Must be 'mean' or 'median'"
     )
   }
 
-  model_out_tbl <- validate_model_out_target_obs(model_out_tbl, target_observations)
+  model_out_tbl <- validate_model_oracle_out(model_out_tbl, oracle_output)
 
   task_id_cols <- get_task_id_cols(model_out_tbl)
   type <- output_type
@@ -26,20 +26,20 @@ transform_point_model_out <- function(model_out_tbl, target_observations, output
     dplyr::filter(output_type == type) |>
     dplyr::rename(model = "model_id")
 
-  if (c("output_type") %in% colnames(target_observations)) {
-    target_observations <- target_observations |>
+  if (c("output_type") %in% colnames(oracle_output)) {
+    oracle_output <- oracle_output |>
       dplyr::filter(output_type == type) |>
       dplyr::select(-c("output_type", "output_type_id"))
   }
 
-  data <- dplyr::left_join(model_out_tbl, target_observations,
-    by = task_id_cols[task_id_cols %in% colnames(target_observations)],
+  data <- dplyr::left_join(model_out_tbl, oracle_output,
+    by = task_id_cols[task_id_cols %in% colnames(oracle_output)],
     relationship = "many-to-one"
   )
 
   forecast_point <- scoringutils::as_forecast_point(data,
     forecast_unit = c("model", task_id_cols),
-    observed = "observation",
+    observed = "oracle_value",
     predicted = "value"
   )
 
diff --git a/R/transform_quantile_model_out.R b/R/transform_quantile_model_out.R
index 009726a..d8bf0e6 100644
--- a/R/transform_quantile_model_out.R
+++ b/R/transform_quantile_model_out.R
@@ -1,11 +1,12 @@
 #' Transform quantile model output into a forecast object
 #'
 #' @param model_out_tbl Model output tibble with predictions
-#' @param target_observations Observed 'ground truth' data to be compared against predictions
+#' @param oracle_output Predictions that would have been generated by an oracle
+#' model that knew the observed target data values in advance
 #'
 #' @return forecast_quantile
-transform_quantile_model_out <- function(model_out_tbl, target_observations) {
-  model_out_tbl <- validate_model_out_target_obs(model_out_tbl, target_observations)
+transform_quantile_model_out <- function(model_out_tbl, oracle_output) {
+  model_out_tbl <- validate_model_oracle_out(model_out_tbl, oracle_output)
 
   task_id_cols <- get_task_id_cols(model_out_tbl)
 
@@ -14,20 +15,20 @@ transform_quantile_model_out <- function(model_out_tbl, target_observations) {
     dplyr::mutate(output_type_id = as.numeric(.data[["output_type_id"]])) |>
     dplyr::rename(model = "model_id")
 
-  if (c("output_type") %in% colnames(target_observations)) {
-    target_observations <- target_observations |>
+  if (c("output_type") %in% colnames(oracle_output)) {
+    oracle_output <- oracle_output |>
       dplyr::filter(.data[["output_type"]] == "quantile") |>
       dplyr::select(-c("output_type", "output_type_id"))
   }
 
-  data <- dplyr::left_join(model_out_tbl, target_observations,
-    by = task_id_cols[task_id_cols %in% colnames(target_observations)],
+  data <- dplyr::left_join(model_out_tbl, oracle_output,
+    by = task_id_cols[task_id_cols %in% colnames(oracle_output)],
     relationship = "many-to-one"
   )
 
   forecast_quantile <- scoringutils::as_forecast_quantile(data,
     forecast_unit = c("model", task_id_cols),
-    observed = "observation",
+    observed = "oracle_value",
     predicted = "value",
     quantile_level = "output_type_id"
   )
diff --git a/R/validate.R b/R/validate.R
index 07db0c7..bad292a 100644
--- a/R/validate.R
+++ b/R/validate.R
@@ -17,15 +17,16 @@ get_task_id_cols <- function(model_out_tbl) {
   return(task_id_cols)
 }
 
-#' Validate model_out_tbl and target_observations arguments to transform functions
+#' Validate model_out_tbl and oracle_output arguments to transform functions
 #'
 #' @param model_out_tbl Model output tibble with predictions
-#' @param target_observations Observed 'ground truth' data to be compared against predictions
+#' @param oracle_output Predictions that would have been generated by an oracle
+#' model that knew the observed target data values in advance
 #'
 #' @return The input `model_out_tbl`, possibly modified to ensure it has S3 class `model_out_tbl`
 #'
 #' @noRd
-validate_model_out_target_obs <- function(model_out_tbl, target_observations) {
+validate_model_oracle_out <- function(model_out_tbl, oracle_output) {
   # check that: model_out_tbl contains columns: model_id, output_type, output_type_id, value
   req_cols <- c("model_id", "output_type", "output_type_id", "value")
   if (!all(req_cols %in% colnames(model_out_tbl))) {
@@ -35,28 +36,28 @@ validate_model_out_target_obs <- function(model_out_tbl, target_observations) {
     )
   }
 
-  # check that model_out_tbl and target_observations have compatible columns
+  # check that model_out_tbl and oracle_output have compatible columns
   task_id_cols <- get_task_id_cols(model_out_tbl)
-  if (length(task_id_cols[task_id_cols %in% colnames(target_observations)]) == 0) {
+  if (length(task_id_cols[task_id_cols %in% colnames(oracle_output)]) == 0) {
     cli::cli_abort(
-      "model_out_tbl and target_observations do not have compatible columns"
+      "model_out_tbl and oracle_output do not have compatible columns"
     )
   }
-  t_o_cols <- colnames(target_observations)
-  expected_cols_superset <- c(task_id_cols, "output_type", "output_type_id", "observation")
+  t_o_cols <- colnames(oracle_output)
+  expected_cols_superset <- c(task_id_cols, "output_type", "output_type_id", "oracle_value")
   unexpected_cols <- t_o_cols[!t_o_cols %in% expected_cols_superset]
   if (length(unexpected_cols) > 0) {
     cli::cli_abort(
       c(
-        "`target_observations` had {length(unexpected_cols)} unexpected column{?s} {.val {unexpected_cols}};",
-        " expected the columns of `target_observations` to be a subset of {.val {expected_cols_superset}}."
+        "`oracle_output` had {length(unexpected_cols)} unexpected column{?s} {.val {unexpected_cols}};",
+        " expected the columns of `oracle_output` to be a subset of {.val {expected_cols_superset}}."
       )
     )
   }
 
-  if (!c("observation") %in% colnames(target_observations)) {
+  if (!c("oracle_value") %in% colnames(oracle_output)) {
     cli::cli_abort(
-      "target_observations does not have observation column"
+      "oracle_output does not have oracle_value column"
     )
   }
 
diff --git a/man/score_model_out.Rd b/man/score_model_out.Rd
index 70b1f60..4f587ec 100644
--- a/man/score_model_out.Rd
+++ b/man/score_model_out.Rd
@@ -6,7 +6,7 @@
 \usage{
 score_model_out(
   model_out_tbl,
-  target_observations,
+  oracle_output,
   metrics = NULL,
   summarize = TRUE,
   by = "model_id",
@@ -16,8 +16,8 @@ score_model_out(
 \arguments{
 \item{model_out_tbl}{Model output tibble with predictions}
 
-\item{target_observations}{Observed 'ground truth' data to be compared to
-predictions}
+\item{oracle_output}{Predictions that would have been generated by an oracle
+model that knew the observed target data values in advance}
 
 \item{metrics}{Character vector of scoring metrics to compute. If \code{NULL}
 (the default), appropriate metrics are chosen automatically. See details
@@ -41,6 +41,9 @@ A data.table with scores
 Scores model outputs with a single \code{output_type} against observed data.
 }
 \details{
+See the hubverse documentation for the expected format of the
+\href{https://hubverse.io/en/latest/user-guide/target-data.html#oracle-output}{oracle output data}.
+
 Default metrics are provided by the \code{scoringutils} package. You can select
 metrics by passing in a character vector of metric names to the \code{metrics}
 argument.
@@ -55,7 +58,6 @@ different \code{output_type}s:
 \item underprediction
 \item dispersion
 \item bias
-\item interval_coverage_deviation
 \item ae_median
 \item "interval_coverage_XX": interval coverage at the "XX" level. For example,
 "interval_coverage_95" is the 95\% interval coverage rate, which would be calculated
@@ -92,7 +94,7 @@ See \link[scoringutils:get_metrics.forecast_point]{scoringutils::get_metrics.for
 quantile_scores <- score_model_out(
   model_out_tbl = hubExamples::forecast_outputs |>
     dplyr::filter(.data[["output_type"]] == "quantile"),
-  target_observations = hubExamples::forecast_target_observations,
+  oracle_output = hubExamples::forecast_oracle_output,
   metrics = c("wis", "interval_coverage_80", "interval_coverage_90"),
   by = "model_id"
 )
@@ -106,7 +108,7 @@ quantile_scores
 pmf_scores <- score_model_out(
   model_out_tbl = hubExamples::forecast_outputs |>
     dplyr::filter(.data[["output_type"]] == "pmf"),
-  target_observations = hubExamples::forecast_target_observations,
+  oracle_output = hubExamples::forecast_oracle_output,
   metrics = "log_score",
   by = c("model_id", "location", "horizon")
 )
@@ -114,6 +116,6 @@ head(pmf_scores)
 \dontshow{\}) # examplesIf}
 }
 \references{
-Making and Evaluating Point Forecasts, Gneiting, Tilmann, 2011,
-Journal of the American Statistical Association.
+Gneiting, Tilmann. 2011. "Making and Evaluating Point Forecasts." Journal of the
+American Statistical Association 106 (494): 746–62. <doi: 10.1198/jasa.2011.r10138>.
 }
diff --git a/man/transform_pmf_model_out.Rd b/man/transform_pmf_model_out.Rd
index d9972c9..bb0a16f 100644
--- a/man/transform_pmf_model_out.Rd
+++ b/man/transform_pmf_model_out.Rd
@@ -6,14 +6,15 @@
 \usage{
 transform_pmf_model_out(
   model_out_tbl,
-  target_observations,
+  oracle_output,
   output_type_id_order = NULL
 )
 }
 \arguments{
 \item{model_out_tbl}{Model output tibble with predictions}
 
-\item{target_observations}{Observed 'ground truth' data to be compared against predictions}
+\item{oracle_output}{Predictions that would have been generated by an oracle
+model that knew the observed target data values in advance}
 
 \item{output_type_id_order}{For nominal variables, this should be \code{NULL} (the default).
 For ordinal variables, this is a vector of levels for pmf forecasts, in
diff --git a/man/transform_point_model_out.Rd b/man/transform_point_model_out.Rd
index 10f0c49..e6f6ac4 100644
--- a/man/transform_point_model_out.Rd
+++ b/man/transform_point_model_out.Rd
@@ -4,12 +4,13 @@
 \alias{transform_point_model_out}
 \title{Transform either mean or median model output into a point forecast object:}
 \usage{
-transform_point_model_out(model_out_tbl, target_observations, output_type)
+transform_point_model_out(model_out_tbl, oracle_output, output_type)
 }
 \arguments{
 \item{model_out_tbl}{Model output tibble with predictions}
 
-\item{target_observations}{Observed 'ground truth' data to be compared against predictions}
+\item{oracle_output}{Predictions that would have been generated by an oracle
+model that knew the observed target data values in advance}
 
 \item{output_type}{Forecast output type: "mean" or "median"}
 }
diff --git a/man/transform_quantile_model_out.Rd b/man/transform_quantile_model_out.Rd
index 6cc07a4..228f012 100644
--- a/man/transform_quantile_model_out.Rd
+++ b/man/transform_quantile_model_out.Rd
@@ -4,12 +4,13 @@
 \alias{transform_quantile_model_out}
 \title{Transform quantile model output into a forecast object}
 \usage{
-transform_quantile_model_out(model_out_tbl, target_observations)
+transform_quantile_model_out(model_out_tbl, oracle_output)
 }
 \arguments{
 \item{model_out_tbl}{Model output tibble with predictions}
 
-\item{target_observations}{Observed 'ground truth' data to be compared against predictions}
+\item{oracle_output}{Predictions that would have been generated by an oracle
+model that knew the observed target data values in advance}
 }
 \value{
 forecast_quantile
diff --git a/tests/testthat/helper-hubex_forecast_oracle_output.R b/tests/testthat/helper-hubex_forecast_oracle_output.R
new file mode 100644
index 0000000..11424ca
--- /dev/null
+++ b/tests/testthat/helper-hubex_forecast_oracle_output.R
@@ -0,0 +1,4039 @@
+# nolint start
+hubex_forecast_oracle_output <- function() {
+  structure(list(location = c("06", "25", "48", "06", "25", "48", 
+  "06", "25", "48", "06", "25", "48", "06", "25", "48", "06", "25", 
+  "48", "06", "25", "48", "06", "25", "48", "06", "25", "48", "06", 
+  "25", "48", "06", "25", "48", "06", "25", "48", "06", "25", "48", 
+  "06", "25", "48", "06", "25", "48", "06", "06", "06", "25", "25", 
+  "25", "48", "48", "48", "06", "06", "06", "25", "25", "25", "48", 
+  "48", "48", "06", "06", "06", "25", "25", "25", "48", "48", "48", 
+  "06", "06", "06", "25", "25", "25", "48", "48", "48", "06", "06", 
+  "06", "25", "25", "25", "48", "48", "48", "06", "06", "06", "25", 
+  "25", "25", "48", "48", "48", "06", "06", "06", "25", "25", "25", 
+  "48", "48", "48", "06", "06", "06", "25", "25", "25", "48", "48", 
+  "48", "06", "06", "06", "25", "25", "25", "48", "48", "48", "06", 
+  "06", "06", "25", "25", "25", "48", "48", "48", "06", "06", "06", 
+  "25", "25", "25", "48", "48", "48", "06", "06", "06", "25", "25", 
+  "25", "48", "48", "48", "06", "06", "06", "25", "25", "25", "48", 
+  "48", "48", "06", "06", "06", "25", "25", "25", "48", "48", "48", 
+  "06", "06", "06", "25", "25", "25", "48", "48", "48", "06", "06", 
+  "06", "06", "25", "25", "25", "25", "48", "48", "48", "48", "06", 
+  "06", "06", "06", "25", "25", "25", "25", "48", "48", "48", "48", 
+  "06", "06", "06", "06", "25", "25", "25", "25", "48", "48", "48", 
+  "48", "06", "06", "06", "06", "25", "25", "25", "25", "48", "48", 
+  "48", "48", "06", "06", "06", "06", "25", "25", "25", "25", "48", 
+  "48", "48", "48", "06", "06", "06", "06", "25", "25", "25", "25", 
+  "48", "48", "48", "48", "06", "06", "06", "06", "25", "25", "25", 
+  "25", "48", "48", "48", "48", "06", "06", "06", "06", "25", "25", 
+  "25", "25", "48", "48", "48", "48", "06", "06", "06", "06", "25", 
+  "25", "25", "25", "48", "48", "48", "48", "06", "06", "06", "06", 
+  "25", "25", "25", "25", "48", "48", "48", "48", "06", "06", "06", 
+  "06", "25", "25", "25", "25", "48", "48", "48", "48", "06", "06", 
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+      "19.5", "19.75", "20", "20.25", "20.5", "20.75", "21", "21.25", 
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+      "5", "5.25", "5.5", "5.75", "6", "6.25", "6.5", "6.75", "7", 
+      "7.25", "7.5", "7.75", "8", "8.25", "8.5", "8.75", "9", "9.25", 
+      "9.5", "9.75", "10", "10.25", "10.5", "10.75", "11", "11.25", 
+      "11.5", "11.75", "12", "12.25", "12.5", "12.75", "13", "13.25", 
+      "13.5", "13.75", "14", "14.25", "14.5", "14.75", "15", "15.25", 
+      "15.5", "15.75", "16", "16.25", "16.5", "16.75", "17", "17.25", 
+      "17.5", "17.75", "18", "18.25", "18.5", "18.75", "19", "19.25", 
+      "19.5", "19.75", "20", "20.25", "20.5", "20.75", "21", "21.25", 
+      "21.5", "21.75", "22", "22.25", "22.5", "22.75", "23", "23.25", 
+      "23.5", "23.75", "24", "24.25", "24.5", "24.75", "25", "0.25", 
+      "0.5", "0.75", "1", "1.25", "1.5", "1.75", "2", "2.25", "2.5", 
+      "2.75", "3", "3.25", "3.5", "3.75", "4", "4.25", "4.5", "4.75", 
+      "5", "5.25", "5.5", "5.75", "6", "6.25", "6.5", "6.75", "7", 
+      "7.25", "7.5", "7.75", "8", "8.25", "8.5", "8.75", "9", "9.25", 
+      "9.5", "9.75", "10", "10.25", "10.5", "10.75", "11", "11.25", 
+      "11.5", "11.75", "12", "12.25", "12.5", "12.75", "13", "13.25", 
+      "13.5", "13.75", "14", "14.25", "14.5", "14.75", "15", "15.25", 
+      "15.5", "15.75", "16", "16.25", "16.5", "16.75", "17", "17.25", 
+      "17.5", "17.75", "18", "18.25", "18.5", "18.75", "19", "19.25", 
+      "19.5", "19.75", "20", "20.25", "20.5", "20.75", "21", "21.25", 
+      "21.5", "21.75", "22", "22.25", "22.5", "22.75", "23", "23.25", 
+      "23.5", "23.75", "24", "24.25", "24.5", "24.75", "25"), oracle_value = c(124, 
+      8, 509, 211, 14, 744, 456, 31, 984, 928, 43, 1129, 1616, 
+      79, 1230, 2790, 221, 1929, 3138, 446, 2139, 2275, 578, 1781, 
+      1576, 694, 1462, 1580, 769, 1225, 1384, 733, 1434, 855, 466, 
+      1170, 354, 238, 731, 198, 122, 458, 116, 71, 341, 124, 124, 
+      124, 8, 8, 8, 509, 509, 509, 211, 211, 211, 14, 14, 14, 744, 
+      744, 744, 456, 456, 456, 31, 31, 31, 984, 984, 984, 928, 
+      928, 928, 43, 43, 43, 1129, 1129, 1129, 1616, 1616, 1616, 
+      79, 79, 79, 1230, 1230, 1230, 2790, 2790, 2790, 221, 221, 
+      221, 1929, 1929, 1929, 3138, 3138, 3138, 446, 446, 446, 2139, 
+      2139, 2139, 2275, 2275, 2275, 578, 578, 578, 1781, 1781, 
+      1781, 1576, 1576, 1576, 694, 694, 694, 1462, 1462, 1462, 
+      1580, 1580, 1580, 769, 769, 769, 1225, 1225, 1225, 1384, 
+      1384, 1384, 733, 733, 733, 1434, 1434, 1434, 855, 855, 855, 
+      466, 466, 466, 1170, 1170, 1170, 354, 354, 354, 238, 238, 
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+      1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+      0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 
+      0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 
+      0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1)), row.names = c(NA, -4860L), spec = structure(list(
+      cols = list(location = structure(list(), class = c("collector_character", 
+      "collector")), target_end_date = structure(list(format = ""), class = c("collector_date", 
+      "collector")), target = structure(list(), class = c("collector_character", 
+      "collector")), output_type = structure(list(), class = c("collector_character", 
+      "collector")), output_type_id = structure(list(), class = c("collector_character", 
+      "collector")), oracle_value = structure(list(), class = c("collector_double", 
+      "collector"))), default = structure(list(), class = c("collector_guess", 
+      "collector")), delim = ","), class = "col_spec"), class = "data.frame")
+}
+# nolint end
diff --git a/tests/testthat/helper-hubex_forecast_outputs.R b/tests/testthat/helper-hubex_forecast_outputs.R
new file mode 100644
index 0000000..486a5cc
--- /dev/null
+++ b/tests/testthat/helper-hubex_forecast_outputs.R
@@ -0,0 +1,14037 @@
+# nolint start
+hubex_forecast_outputs <- function() {
+  structure(list(model_id = c("Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
+  "Flusight-baseline", "Flusight-baseline", "Flusight-baseline", 
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+      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
+      1, 1, 1, 1, 1, 1, 1, 1, 1)), row.names = c(NA, -10224L), class = "data.frame")
+}
+# nolint end
diff --git a/tests/testthat/helper-pmf_test_oracle_output.R b/tests/testthat/helper-pmf_test_oracle_output.R
new file mode 100644
index 0000000..7c2a795
--- /dev/null
+++ b/tests/testthat/helper-pmf_test_oracle_output.R
@@ -0,0 +1,17 @@
+# nolint start
+pmf_test_oracle_output <- function() {
+  structure(list(location = c("US", "01", "US", "01", "US", "01", 
+  "US", "01", "US", "01", "US", "01", "US", "01", "US", "01", "US", 
+  "01", "US", "01", "US", "01", "US", "01"), target_date = structure(c(18275, 
+  18275, 18282, 18282, 18289, 18289, 18296, 18296, 18275, 18275, 
+  18282, 18282, 18289, 18289, 18296, 18296, 18275, 18275, 18282, 
+  18282, 18289, 18289, 18296, 18296), class = "Date"), output_type_id = c("cat", 
+  "cat", "cat", "cat", "cat", "cat", "cat", "cat", "dog", "dog", 
+  "dog", "dog", "dog", "dog", "dog", "dog", "bird", "bird", "bird", 
+  "bird", "bird", "bird", "bird", "bird"), observation = c(1, NA, 
+  NA, NA, 1, NA, NA, 1, NA, NA, 1, 1, NA, 1, NA, NA, NA, 1, NA, 
+  NA, NA, NA, 1, NA), oracle_value = c(1, 0, 0, 0, 1, 0, 0, 1, 
+  0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)), row.names = c(NA, 
+  -24L), class = c("tbl_df", "tbl", "data.frame"))
+}
+# nolint end
diff --git a/tests/testthat/helper-pmf_test_target_observations.R b/tests/testthat/helper-pmf_test_target_observations.R
index 991c8d0..3e7fe07 100644
--- a/tests/testthat/helper-pmf_test_target_observations.R
+++ b/tests/testthat/helper-pmf_test_target_observations.R
@@ -1,5 +1,5 @@
 # nolint start
-pmf_test_target_observations <- function() {
+pmf_test_oracle_output <- function() {
   structure(list(location = c("US", "01", "US", "01", "US", "01", 
   "US", "01", "US", "01", "US", "01", "US", "01", "US", "01", "US", 
   "01", "US", "01", "US", "01", "US", "01"), target_date = structure(c(18275, 
@@ -8,7 +8,7 @@ pmf_test_target_observations <- function() {
   18282, 18289, 18289, 18296, 18296), class = "Date"), output_type_id = c("cat", 
   "cat", "cat", "cat", "cat", "cat", "cat", "cat", "dog", "dog", 
   "dog", "dog", "dog", "dog", "dog", "dog", "bird", "bird", "bird", 
-  "bird", "bird", "bird", "bird", "bird"), observation = c(1, 0, 
+  "bird", "bird", "bird", "bird", "bird"), oracle_value = c(1, 0, 
   0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 
   0)), row.names = c(NA, -24L), class = c("tbl_df", "tbl", "data.frame"
   ))
diff --git a/tests/testthat/test-score_model_out.R b/tests/testthat/test-score_model_out.R
index 83ed860..65bc37e 100644
--- a/tests/testthat/test-score_model_out.R
+++ b/tests/testthat/test-score_model_out.R
@@ -1,23 +1,23 @@
 test_that("score_model_out succeeds with valid inputs: mean output_type, default metrics, by all", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   act_scores <- score_model_out(
     model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "mean"),
-    target_observations = forecast_target_observations,
+    oracle_output = forecast_oracle_output,
     by = c("model_id", "location", "reference_date", "horizon", "target_end_date", "target")
   )
 
   exp_scores <- forecast_outputs |>
     dplyr::filter(.data[["output_type"]] == "mean") |>
     dplyr::left_join(
-      forecast_target_observations |>
+      forecast_oracle_output |>
         dplyr::filter(.data[["output_type"]] == "mean"),
       by = c("location", "target_end_date", "target")
     ) |>
     dplyr::mutate(
-      se = (.data[["value"]] - .data[["observation"]])^2
+      se = (.data[["value"]] - .data[["oracle_value"]])^2
     ) |>
     dplyr::group_by(dplyr::across(dplyr::all_of(
       c("model_id", "location", "reference_date", "horizon", "target_end_date", "target")
@@ -41,27 +41,27 @@ test_that("score_model_out succeeds with valid inputs: mean output_type, default
 
 test_that("score_model_out succeeds with valid inputs: mean output_type, default metrics, summarize FALSE", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   act_scores <- score_model_out(
     model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "mean"),
-    target_observations = forecast_target_observations,
+    oracle_output = forecast_oracle_output,
     summarize = FALSE
   )
 
   exp_scores <- forecast_outputs |>
     dplyr::filter(.data[["output_type"]] == "mean") |>
     dplyr::left_join(
-      forecast_target_observations |>
+      forecast_oracle_output |>
         dplyr::filter(.data[["output_type"]] == "mean"),
       by = c("location", "target_end_date", "target")
     ) |>
     dplyr::mutate(
-      se = (.data[["value"]] - .data[["observation"]])^2
+      se = (.data[["value"]] - .data[["oracle_value"]])^2
     ) |>
     dplyr::group_by(dplyr::across(dplyr::all_of(
-      c("model_id", "location", "reference_date", "horizon", "target_end_date", "target")
+      c("model_id", "reference_date", "target", "horizon", "location", "target_end_date")
     ))) |>
     dplyr::summarize(
       se_point = mean(.data[["se"]]),
@@ -82,12 +82,12 @@ test_that("score_model_out succeeds with valid inputs: mean output_type, default
 
 test_that("score_model_out succeeds with valid inputs: mean output_type, character metrics, custom by", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   act_scores <- score_model_out(
     model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "mean"),
-    target_observations = forecast_target_observations,
+    oracle_output = forecast_oracle_output,
     metrics = c("ae_point", "se_point"),
     by = c("model_id", "location")
   )
@@ -95,13 +95,13 @@ test_that("score_model_out succeeds with valid inputs: mean output_type, charact
   exp_scores <- forecast_outputs |>
     dplyr::filter(.data[["output_type"]] == "mean") |>
     dplyr::left_join(
-      forecast_target_observations |>
+      forecast_oracle_output |>
         dplyr::filter(.data[["output_type"]] == "mean"),
       by = c("location", "target_end_date", "target")
     ) |>
     dplyr::mutate(
-      ae = abs(.data[["value"]] - .data[["observation"]]),
-      se = (.data[["value"]] - .data[["observation"]])^2
+      ae = abs(.data[["value"]] - .data[["oracle_value"]]),
+      se = (.data[["value"]] - .data[["oracle_value"]])^2
     ) |>
     dplyr::group_by(dplyr::across(dplyr::all_of(
       c("model_id", "location")
@@ -126,27 +126,27 @@ test_that("score_model_out succeeds with valid inputs: mean output_type, charact
 
 test_that("score_model_out succeeds with valid inputs: median output_type, default metrics, summarize FALSE", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   act_scores <- score_model_out(
     model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "median"),
-    target_observations = forecast_target_observations,
+    oracle_output = forecast_oracle_output,
     summarize = FALSE
   )
 
   exp_scores <- forecast_outputs |>
     dplyr::filter(.data[["output_type"]] == "median") |>
     dplyr::left_join(
-      forecast_target_observations |>
+      forecast_oracle_output |>
         dplyr::filter(.data[["output_type"]] == "median"),
       by = c("location", "target_end_date", "target")
     ) |>
     dplyr::mutate(
-      ae = abs(.data[["value"]] - .data[["observation"]])
+      ae = abs(.data[["value"]] - .data[["oracle_value"]])
     ) |>
     dplyr::group_by(dplyr::across(dplyr::all_of(
-      c("model_id", "location", "reference_date", "horizon", "target_end_date", "target")
+      c("model_id", "reference_date", "target", "horizon", "location", "target_end_date")
     ))) |>
     dplyr::summarize(
       ae_point = mean(.data[["ae"]]),
@@ -167,12 +167,12 @@ test_that("score_model_out succeeds with valid inputs: median output_type, defau
 
 test_that("score_model_out succeeds with valid inputs: quantile output_type, wis and interval metrics, custom by", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   act_scores <- score_model_out(
     model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-    target_observations = forecast_target_observations,
+    oracle_output = forecast_oracle_output,
     metrics = c("wis", "interval_coverage_80", "interval_coverage_90"),
     by = c("model_id", "location")
   )
@@ -180,7 +180,7 @@ test_that("score_model_out succeeds with valid inputs: quantile output_type, wis
   exp_scores <- forecast_outputs |>
     dplyr::filter(.data[["output_type"]] == "quantile") |>
     dplyr::left_join(
-      forecast_target_observations |>
+      forecast_oracle_output |>
         dplyr::filter(.data[["output_type"]] == "quantile") |>
         dplyr::select(-dplyr::all_of(c("output_type", "output_type_id"))),
       by = c("location", "target_end_date", "target")
@@ -188,28 +188,28 @@ test_that("score_model_out succeeds with valid inputs: quantile output_type, wis
     dplyr::mutate(
       output_type_id = as.numeric(.data[["output_type_id"]]),
       qs = ifelse(
-        .data[["observation"]] >= .data[["value"]],
-        .data[["output_type_id"]] * (.data[["observation"]] - .data[["value"]]),
-        (1 - .data[["output_type_id"]]) * (.data[["value"]] - .data[["observation"]])
+        .data[["oracle_value"]] >= .data[["value"]],
+        .data[["output_type_id"]] * (.data[["oracle_value"]] - .data[["value"]]),
+        (1 - .data[["output_type_id"]]) * (.data[["value"]] - .data[["oracle_value"]])
       ),
       q_coverage_80_lower = ifelse(
         .data[["output_type_id"]] == 0.1,
-        .data[["observation"]] >= .data[["value"]],
+        .data[["oracle_value"]] >= .data[["value"]],
         NA_real_
       ),
       q_coverage_80_upper = ifelse(
         .data[["output_type_id"]] == 0.9,
-        .data[["observation"]] <= .data[["value"]],
+        .data[["oracle_value"]] <= .data[["value"]],
         NA_real_
       ),
       q_coverage_90_lower = ifelse(
         .data[["output_type_id"]] == 0.05,
-        .data[["observation"]] >= .data[["value"]],
+        .data[["oracle_value"]] >= .data[["value"]],
         NA_real_
       ),
       q_coverage_90_upper = ifelse(
         .data[["output_type_id"]] == 0.95,
-        .data[["observation"]] <= .data[["value"]],
+        .data[["oracle_value"]] <= .data[["value"]],
         NA_real_
       )
     ) |>
@@ -249,12 +249,12 @@ test_that("score_model_out succeeds with valid inputs: quantile output_type, wis
 
 test_that("score_model_out succeeds with valid inputs: quantile output_type, wis/interval metrics, summarize FALSE", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   act_scores <- score_model_out(
     model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-    target_observations = forecast_target_observations,
+    oracle_output = forecast_oracle_output,
     metrics = c("wis", "interval_coverage_80", "interval_coverage_90"),
     summarize = FALSE
   )
@@ -262,7 +262,7 @@ test_that("score_model_out succeeds with valid inputs: quantile output_type, wis
   exp_scores <- forecast_outputs |>
     dplyr::filter(.data[["output_type"]] == "quantile") |>
     dplyr::left_join(
-      forecast_target_observations |>
+      forecast_oracle_output |>
         dplyr::filter(.data[["output_type"]] == "quantile") |>
         dplyr::select(-dplyr::all_of(c("output_type", "output_type_id"))),
       by = c("location", "target_end_date", "target")
@@ -270,33 +270,33 @@ test_that("score_model_out succeeds with valid inputs: quantile output_type, wis
     dplyr::mutate(
       output_type_id = as.numeric(.data[["output_type_id"]]),
       qs = ifelse(
-        .data[["observation"]] >= .data[["value"]],
-        .data[["output_type_id"]] * (.data[["observation"]] - .data[["value"]]),
-        (1 - .data[["output_type_id"]]) * (.data[["value"]] - .data[["observation"]])
+        .data[["oracle_value"]] >= .data[["value"]],
+        .data[["output_type_id"]] * (.data[["oracle_value"]] - .data[["value"]]),
+        (1 - .data[["output_type_id"]]) * (.data[["value"]] - .data[["oracle_value"]])
       ),
       q_coverage_80_lower = ifelse(
         .data[["output_type_id"]] == 0.1,
-        .data[["observation"]] >= .data[["value"]],
+        .data[["oracle_value"]] >= .data[["value"]],
         NA_real_
       ),
       q_coverage_80_upper = ifelse(
         .data[["output_type_id"]] == 0.9,
-        .data[["observation"]] <= .data[["value"]],
+        .data[["oracle_value"]] <= .data[["value"]],
         NA_real_
       ),
       q_coverage_90_lower = ifelse(
         .data[["output_type_id"]] == 0.05,
-        .data[["observation"]] >= .data[["value"]],
+        .data[["oracle_value"]] >= .data[["value"]],
         NA_real_
       ),
       q_coverage_90_upper = ifelse(
         .data[["output_type_id"]] == 0.95,
-        .data[["observation"]] <= .data[["value"]],
+        .data[["oracle_value"]] <= .data[["value"]],
         NA_real_
       )
     ) |>
     dplyr::group_by(dplyr::across(dplyr::all_of(
-      c("model_id", "location", "reference_date", "horizon", "target_end_date", "target")
+      c("model_id", "reference_date", "target", "horizon", "location", "target_end_date")
     ))) |>
     dplyr::summarize(
       wis = 2 * mean(.data[["qs"]]),
@@ -323,24 +323,24 @@ test_that("score_model_out succeeds with valid inputs: quantile output_type, wis
 
 test_that("score_model_out succeeds with valid inputs: nominal pmf output_type, default metrics, custom by", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   act_scores <- score_model_out(
     model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "pmf"),
-    target_observations = forecast_target_observations,
+    oracle_output = forecast_oracle_output,
     by = c("model_id", "location")
   )
 
   exp_scores <- forecast_outputs |>
     dplyr::filter(.data[["output_type"]] == "pmf") |>
     dplyr::left_join(
-      forecast_target_observations |>
+      forecast_oracle_output |>
         dplyr::filter(.data[["output_type"]] == "pmf") |>
         dplyr::select(-dplyr::all_of(c("output_type"))),
       by = c("location", "target_end_date", "target", "output_type_id")
     ) |>
-    dplyr::filter(.data[["observation"]] == 1) |>
+    dplyr::filter(.data[["oracle_value"]] == 1) |>
     dplyr::mutate(
       log_score = -1 * log(.data[["value"]])
     ) |>
@@ -366,13 +366,13 @@ test_that("score_model_out succeeds with valid inputs: nominal pmf output_type,
 
 test_that("score_model_out errors when model_out_tbl has multiple output_types", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   expect_error(
     score_model_out(
       model_out_tbl = forecast_outputs,
-      target_observations = forecast_target_observations
+      oracle_output = forecast_oracle_output
     ),
     regexp = "model_out_tbl must contain a single output_type, but it has multiple"
   )
@@ -381,13 +381,13 @@ test_that("score_model_out errors when model_out_tbl has multiple output_types",
 
 test_that("score_model_out works with all kinds of interval levels are requested", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   expect_error(
     score_model_out(
       model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-      target_observations = forecast_target_observations,
+      oracle_output = forecast_oracle_output,
       metrics = "interval_coverage_5d2a"
     ),
     regexp = "must be a number between 0 and 100"
@@ -397,7 +397,7 @@ test_that("score_model_out works with all kinds of interval levels are requested
     expect_warning(
       score_model_out(
         model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-        target_observations = forecast_target_observations,
+        oracle_output = forecast_oracle_output,
         metrics = "interval_coverage_55"
       ),
       "To compute the interval coverage for an interval range of" #scoringutils warning
@@ -406,7 +406,7 @@ test_that("score_model_out works with all kinds of interval levels are requested
     expect_error(
       score_model_out(
         model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-        target_observations = forecast_target_observations,
+        oracle_output = forecast_oracle_output,
         metrics = "interval_coverage_100"
       ),
       regexp = "must be a number between 0 and 100"
@@ -415,7 +415,7 @@ test_that("score_model_out works with all kinds of interval levels are requested
     expect_warning(
       score_model_out(
         model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-        target_observations = forecast_target_observations,
+        oracle_output = forecast_oracle_output,
         metrics = "interval_coverage_5.3"
       ),
       "To compute the interval coverage for an interval range of" #scoringutils warning
@@ -426,13 +426,13 @@ test_that("score_model_out works with all kinds of interval levels are requested
 
 test_that("score_model_out errors when invalid metrics are requested", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   expect_error(
     score_model_out(
       model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-      target_observations = forecast_target_observations,
+      oracle_output = forecast_oracle_output,
       metrics = "log_score"
     ),
     regexp = "has additional elements"
@@ -441,7 +441,7 @@ test_that("score_model_out errors when invalid metrics are requested", {
   expect_error(
     score_model_out(
       model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-      target_observations = forecast_target_observations,
+      oracle_output = forecast_oracle_output,
       metrics = list(5, 6, "asdf")
     ),
     regexp =
@@ -451,7 +451,7 @@ test_that("score_model_out errors when invalid metrics are requested", {
   expect_error(
     score_model_out(
       model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-      target_observations = forecast_target_observations,
+      oracle_output = forecast_oracle_output,
       metrics = c("asdfinterval_coverage_90")
     ),
     regexp =
@@ -461,7 +461,7 @@ test_that("score_model_out errors when invalid metrics are requested", {
   expect_error(
     score_model_out(
       model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "mean"),
-      target_observations = forecast_target_observations,
+      oracle_output = forecast_oracle_output,
       metrics = scoringutils::get_metrics(scoringutils::example_point),
       by = c("model_id", "location")
     ),
@@ -474,13 +474,13 @@ test_that("score_model_out errors when invalid metrics are requested", {
 
 test_that("score_model_out errors when an unsupported output_type is provided", {
   # Forecast data from HubExamples: <https://hubverse-org.github.io/hubExamples/reference/forecast_data.html>
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
+  forecast_outputs <- hubex_forecast_outputs()
+  forecast_oracle_output <- hubex_forecast_oracle_output()
 
   expect_error(
     score_model_out(
       model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "cdf"),
-      target_observations = forecast_target_observations,
+      oracle_output = forecast_oracle_output,
       metrics = "log_score"
     ),
     regexp = "only supports the following types"
diff --git a/tests/testthat/test-transform_pmf_model_out.R b/tests/testthat/test-transform_pmf_model_out.R
index 2af66d4..e5eaacd 100644
--- a/tests/testthat/test-transform_pmf_model_out.R
+++ b/tests/testthat/test-transform_pmf_model_out.R
@@ -2,33 +2,33 @@
 # tests/testthat/testdata/make_pmf_test_data.R, and can be loaded by the calls
 #
 # model_out_tbl <- pmf_test_model_out_tbl()
-# target_observations <- pmf_test_target_observations()
+# oracle_output <- pmf_test_oracle_output()
 # exp_forecast <- pmf_test_exp_forecast()
 #
-# By construction, model_out_tbl and target_observations are valid inputs to transform_pmf_model_out,
+# By construction, model_out_tbl and oracle_output are valid inputs to transform_pmf_model_out,
 # and exp_forecast is the expected return value from transform_pmf_model_out
 
 test_that("transform_pmf_model_out succeeds with valid inputs", {
   model_out_tbl <- pmf_test_model_out_tbl()
-  target_observations <- pmf_test_target_observations()
+  oracle_output <- pmf_test_oracle_output()
   exp_forecast <- pmf_test_exp_forecast()
 
   act_forecast <- transform_pmf_model_out(
     model_out_tbl = model_out_tbl,
-    target_observations = target_observations
+    oracle_output = oracle_output
   )
 
   expect_equal(act_forecast, exp_forecast)
 })
 test_that("output_type_id_order is unsupported (fix when supported)", {
   model_out_tbl <- pmf_test_model_out_tbl()
-  target_observations <- pmf_test_target_observations()
+  oracle_output <- pmf_test_oracle_output()
   exp_forecast <- pmf_test_exp_forecast()
 
   expect_error(
     act_forecast <- transform_pmf_model_out(
       model_out_tbl = model_out_tbl,
-      target_observations = target_observations,
+      oracle_output = oracle_output,
       output_type_id_order = "excellence"
     ),
     regexp = "not yet supported"
@@ -38,14 +38,14 @@ test_that("output_type_id_order is unsupported (fix when supported)", {
 test_that("transform_pmf_model_out doesn't depend on specific column names for task id variables", {
   model_out_tbl <- pmf_test_model_out_tbl() |>
     dplyr::rename(loc = location, date = target_date)
-  target_observations <- pmf_test_target_observations() |>
+  oracle_output <- pmf_test_oracle_output() |>
     dplyr::rename(loc = location, date = target_date)
   exp_forecast <- pmf_test_exp_forecast() |>
     dplyr::rename(loc = location, date = target_date)
 
   act_forecast <- transform_pmf_model_out(
     model_out_tbl = model_out_tbl,
-    target_observations = target_observations
+    oracle_output = oracle_output
   )
 
   expect_equal(act_forecast, exp_forecast)
@@ -55,26 +55,26 @@ test_that("transform_pmf_model_out doesn't depend on specific column names for t
 test_that("transform_pmf_model_out throws an error if model_out_tbl has no rows", {
   # Error is thrown by checkmate::assert_data_frame() via scoringutils::assert_forecast_generic()
   model_out_tbl <- pmf_test_model_out_tbl()
-  target_observations <- pmf_test_target_observations()
+  oracle_output <- pmf_test_oracle_output()
   expect_error(
     suppressWarnings(transform_pmf_model_out(
       model_out_tbl = model_out_tbl[0, ],
-      target_observations = target_observations
+      oracle_output = oracle_output
     )),
     regexp = "Must have at least 1 rows, but has 0 rows."
   )
 })
 
 
-test_that("many-to-one relationship exists between model_out_tbl and target_observations", {
+test_that("many-to-one relationship exists between model_out_tbl and oracle_output", {
   model_out_tbl <- pmf_test_model_out_tbl()
-  target_observations <- pmf_test_target_observations() |>
+  oracle_output <- pmf_test_oracle_output() |>
     dplyr::select(-location)
 
   expect_error(
     suppressMessages(transform_pmf_model_out(
       model_out_tbl = model_out_tbl,
-      target_observations = target_observations
+      oracle_output = oracle_output
     )),
     regexp = "Each row in `x` must match at most 1 row in `y`."
   )
diff --git a/tests/testthat/test-transform_point_model_out.R b/tests/testthat/test-transform_point_model_out.R
index 1bc6aa2..592ab5b 100644
--- a/tests/testthat/test-transform_point_model_out.R
+++ b/tests/testthat/test-transform_point_model_out.R
@@ -5,7 +5,7 @@ test_that("inputs are valid", {
   expect_error(
     suppressMessages(transform_point_model_out(
       model_out_tbl = model_out_tbl_1,
-      target_observations = data.frame(),
+      oracle_output = data.frame(),
       output_type = NULL
     )),
     regexp = "invalid 'output_type': Must be 'mean' or 'median'"
@@ -13,7 +13,7 @@ test_that("inputs are valid", {
   expect_error(
     suppressMessages(transform_point_model_out(
       model_out_tbl = model_out_tbl_1,
-      target_observations = data.frame(),
+      oracle_output = data.frame(),
       output_type = 1
     )),
     regexp = "invalid 'output_type': 1 Must be 'mean' or 'median'"
@@ -27,12 +27,13 @@ test_that("model_out_tbl_1 output is valid", {
   model_out_tbl_1 <- utils::read.csv(
     test_path("testdata/model_out_tbl_point_1.csv")
   )
-  target_observations_1 <- utils::read.csv(
+  oracle_output_1 <- utils::read.csv(
     test_path("testdata/target_data_1.csv")
-  )
+  ) |>
+    dplyr::rename(oracle_value = observation)
   act_forecast <- transform_point_model_out(
     model_out_tbl = model_out_tbl_1,
-    target_observations = target_observations_1,
+    oracle_output = oracle_output_1,
     output_type = "mean"
   )
   exp_forecast <- utils::read.csv(
@@ -50,13 +51,13 @@ test_that("transform_point_model_out() throws error for unexpected columns", {
   model_out_tbl_1 <- utils::read.csv(test_path("testdata/model_out_tbl_point_1.csv")) |>
     dplyr::rename(loc = location, trgt = target, date = target_end_date)
 
-  target_observations_1 <- utils::read.csv(test_path("testdata/target_data_1.csv")) |>
+  oracle_output_1 <- utils::read.csv(test_path("testdata/target_data_1.csv")) |>
     dplyr::rename(date = target_end_date) # leave location column unchanged
 
   expect_error(
     suppressMessages(transform_point_model_out(
       model_out_tbl = model_out_tbl_1,
-      target_observations = target_observations_1,
+      oracle_output = oracle_output_1,
       output_type = "mean"
     )),
     regexp = "unexpected column"
@@ -68,12 +69,12 @@ test_that("transform_point_model_out() works with modified column names", {
   model_out_tbl_1 <- utils::read.csv(test_path("testdata/model_out_tbl_point_1.csv")) |>
     dplyr::rename(loc = location, trgt = target, date = target_end_date)
 
-  target_observations_1 <- utils::read.csv(test_path("testdata/target_data_1.csv")) |>
-    dplyr::rename(loc = location, date = target_end_date)
+  oracle_output_1 <- utils::read.csv(test_path("testdata/target_data_1.csv")) |>
+    dplyr::rename(loc = location, date = target_end_date, oracle_value = observation)
 
   act_forecast <- transform_point_model_out(
     model_out_tbl = model_out_tbl_1,
-    target_observations = target_observations_1,
+    oracle_output = oracle_output_1,
     output_type = "mean"
   )
   exp_forecast <- utils::read.csv(test_path("testdata/exp_forecast_1.csv")) |>
@@ -87,13 +88,14 @@ test_that("Errors is thrown when model_out_tbl_1 has no rows", {
   model_out_tbl_1 <- utils::read.csv(
     test_path("testdata/model_out_tbl_point_1.csv")
   )
-  target_observations_1 <- utils::read.csv(
+  oracle_output_1 <- utils::read.csv(
     test_path("testdata/target_data_1.csv")
-  )
+  ) |>
+    dplyr::rename(oracle_value = observation)
   expect_error(
     suppressWarnings(transform_point_model_out(
       model_out_tbl = model_out_tbl_1[0, ],
-      target_observations = target_observations_1,
+      oracle_output = oracle_output_1,
       output_type = "mean"
     )),
     regexp = "Must have at least 1 rows, but has 0 rows."
@@ -101,18 +103,19 @@ test_that("Errors is thrown when model_out_tbl_1 has no rows", {
 })
 
 
-test_that("many-to-one relationship exists between model_out_tbl and target_observations", {
+test_that("many-to-one relationship exists between model_out_tbl and oracle_output", {
   model_out_tbl_1 <- utils::read.csv(
     test_path("testdata/model_out_tbl_point_1.csv")
   )
-  target_observations_2 <- utils::read.csv(
+  oracle_output_2 <- utils::read.csv(
     test_path("testdata/target_data_2.csv")
-  )
+  ) |>
+    dplyr::rename(oracle_value = observation)
 
   expect_error(
     suppressMessages(transform_point_model_out(
       model_out_tbl = model_out_tbl_1,
-      target_observations = target_observations_2,
+      oracle_output = oracle_output_2,
       output_type = "mean"
     )),
     regexp = "Each row in `x` must match at most 1 row in `y`."
@@ -120,29 +123,38 @@ test_that("many-to-one relationship exists between model_out_tbl and target_obse
 })
 
 test_that("hubExamples data set is transformed correctly", {
-  # forecast_outputs.rda & forecast_target_observations.rda are stored in hubExamples:
+  # forecast_outputs.rda & forecast_oracle_output.rda are stored in hubExamples:
   # https://github.com/hubverse-org/hubExamples/tree/main
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
-  model_out_tbl <- forecast_outputs
-  target_observations <- forecast_target_observations
+  model_out_tbl <- hubex_forecast_outputs()
+  oracle_output <- hubex_forecast_oracle_output()
   act_forecast <- transform_point_model_out(
     model_out_tbl = model_out_tbl,
-    target_observations = target_observations,
+    oracle_output = oracle_output,
     output_type = "mean"
   )
 
-  exp_forecast <- utils::read.csv(
-    test_path("testdata/exp_forecast_hubExamples_1.csv")
-  ) |>
-    dplyr::mutate(
-      location = as.character(location),
-      reference_date = as.Date(reference_date, "%Y-%m-%d"),
-      target_end_date = as.Date(target_end_date, "%Y-%m-%d")
-    )
+  exp_forecast <- model_out_tbl |>
+    dplyr::filter(output_type == "mean") |>
+    dplyr::rename(model = model_id)
+
   expect_s3_class(
     act_forecast,
     c("forecast_point", "forecast", "data.table", "data.frame")
   )
-  expect_equal(as.data.frame(act_forecast), as.data.frame(exp_forecast))
+  # same number of row as mean predictions from model_out_tbl,
+  # same predicted values
+  expect_equal(nrow(exp_forecast), nrow(act_forecast))
+  exp_act_forecast <- dplyr::full_join(
+    exp_forecast, act_forecast,
+    by = c("model", "reference_date", "target", "horizon", "location", "target_end_date")
+  )
+  expect_equal(exp_act_forecast$predicted, exp_act_forecast$value)
+
+  # correct observed values, in alignment with oracle_output
+  exp_act_forecast <- dplyr::left_join(
+    act_forecast,
+    oracle_output |> dplyr::filter(output_type == "mean"),
+    by = c("target", "location", "target_end_date")
+  )
+  expect_equal(exp_act_forecast$observed, exp_act_forecast$oracle_value)
 })
diff --git a/tests/testthat/test-transform_quantile_model_out.R b/tests/testthat/test-transform_quantile_model_out.R
index 0e9f14b..ca61c04 100644
--- a/tests/testthat/test-transform_quantile_model_out.R
+++ b/tests/testthat/test-transform_quantile_model_out.R
@@ -1,9 +1,10 @@
 test_that("model_out_tbl_1 output is valid", {
   model_out_tbl_1 <- utils::read.csv(test_path("testdata/model_out_tbl_quantile_1.csv"))
-  target_observations_1 <- utils::read.csv(test_path("testdata/target_data_1.csv"))
+  oracle_output_1 <- utils::read.csv(test_path("testdata/target_data_1.csv")) |>
+    dplyr::rename(oracle_value = observation)
   act_forecast <- transform_quantile_model_out(
     model_out_tbl = model_out_tbl_1,
-    target_observations = target_observations_1
+    oracle_output = oracle_output_1
   )
 
   exp_forecast <- utils::read.csv(test_path("testdata/exp_forecast_2.csv"))
@@ -18,13 +19,13 @@ test_that("model_out_tbl_1 columns are valid", {
   ) |>
     dplyr::rename(loc = location, trgt = target, date = target_end_date)
 
-  target_observations_1 <- utils::read.csv(
+  oracle_output_1 <- utils::read.csv(
     test_path("testdata/target_data_1.csv")
   ) |>
-    dplyr::rename(loc = location, date = target_end_date)
+    dplyr::rename(loc = location, date = target_end_date, oracle_value = observation)
   act_forecast <- transform_quantile_model_out(
     model_out_tbl = model_out_tbl_1,
-    target_observations = target_observations_1
+    oracle_output = oracle_output_1
   )
   exp_forecast <- utils::read.csv(
     test_path("testdata/exp_forecast_2.csv")
@@ -39,59 +40,74 @@ test_that("model_out_tbl_1 has any rows", {
   model_out_tbl_1 <- utils::read.csv(
     test_path("testdata/model_out_tbl_quantile_1.csv")
   )
-  target_observations_1 <- utils::read.csv(
+  oracle_output_1 <- utils::read.csv(
     test_path("testdata/target_data_1.csv")
-  )
+  ) |>
+    dplyr::rename(oracle_value = observation)
   expect_error(
     suppressWarnings(transform_quantile_model_out(
       model_out_tbl = model_out_tbl_1[0, ],
-      target_observations = target_observations_1
+      oracle_output = oracle_output_1
     )),
     regexp = "Must have at least 1 rows, but has 0 rows."
   )
 })
 
 
-test_that("many-to-one relationship exists between model_out_tbl and target_observations", {
+test_that("many-to-one relationship exists between model_out_tbl and oracle_output", {
   model_out_tbl_1 <- utils::read.csv(
     test_path("testdata/model_out_tbl_quantile_1.csv")
   )
-  target_observations_2 <- utils::read.csv(
+  oracle_output_2 <- utils::read.csv(
     test_path("testdata/target_data_2.csv")
-  )
+  ) |>
+    dplyr::rename(oracle_value = observation)
 
   expect_error(
     suppressMessages(transform_quantile_model_out(
       model_out_tbl = model_out_tbl_1,
-      target_observations = target_observations_2
+      oracle_output = oracle_output_2
     )),
     regexp = "Each row in `x` must match at most 1 row in `y`."
   )
 })
 
 test_that("hubExamples data set is transformed correctly", {
-  # forecast_outputs.rda & forecast_target_observations.rda are stored in hubExamples:
+  # forecast_outputs.rda & forecast_oracle_output.rda are stored in hubExamples:
   # https://github.com/hubverse-org/hubExamples/tree/main
-  load(test_path("testdata/forecast_outputs.rda")) # sets forecast_outputs
-  load(test_path("testdata/forecast_target_observations.rda")) # sets forecast_target_observations
-  model_out_tbl <- forecast_outputs
-  target_observations <- forecast_target_observations
+  model_out_tbl <- hubex_forecast_outputs()
+  oracle_output <- hubex_forecast_oracle_output()
   act_forecast <- transform_quantile_model_out(
     model_out_tbl = model_out_tbl,
-    target_observations = target_observations
+    oracle_output = oracle_output
   )
 
-  exp_forecast <- utils::read.csv(
-    test_path("testdata/exp_forecast_hubExamples_2.csv")
-  ) |>
-    dplyr::mutate(
-      location = as.character(location),
-      reference_date = as.Date(reference_date, "%Y-%m-%d"),
-      target_end_date = as.Date(target_end_date, "%Y-%m-%d")
-    )
+  exp_forecast <- model_out_tbl |>
+    dplyr::filter(output_type == "quantile") |>
+    dplyr::rename(model = model_id, quantile_level = output_type_id) |>
+    dplyr::mutate(quantile_level = as.numeric(quantile_level))
   expect_s3_class(
     act_forecast,
     c("forecast_quantile", "forecast", "data.table", "data.frame")
   )
-  expect_equal(as.data.frame(act_forecast), as.data.frame(exp_forecast))
+
+  # same number of row as quantile predictions from model_out_tbl,
+  # same predicted values
+  expect_equal(nrow(exp_forecast), nrow(act_forecast))
+  exp_act_forecast <- dplyr::full_join(
+    exp_forecast, act_forecast,
+    by = c("model", "reference_date", "target", "horizon", "location", "target_end_date", "quantile_level")
+  )
+  expect_equal(exp_act_forecast$predicted, exp_act_forecast$value)
+
+  # correct observed values, in alignment with oracle_output
+  exp_act_forecast <- dplyr::left_join(
+    act_forecast,
+    oracle_output |>
+      dplyr::filter(output_type == "quantile") |>
+      dplyr::rename(quantile_level = output_type_id) |>
+      dplyr::mutate(quantile_level = as.numeric(quantile_level)),
+    by = c("target", "location", "target_end_date")
+  )
+  expect_equal(exp_act_forecast$observed, exp_act_forecast$oracle_value)
 })
diff --git a/tests/testthat/test-validate_model_out_target_obs.R b/tests/testthat/test-validate_model_oracle_out.R
similarity index 62%
rename from tests/testthat/test-validate_model_out_target_obs.R
rename to tests/testthat/test-validate_model_oracle_out.R
index b77ccec..e47d202 100644
--- a/tests/testthat/test-validate_model_out_target_obs.R
+++ b/tests/testthat/test-validate_model_oracle_out.R
@@ -1,22 +1,22 @@
-test_that("validate_model_out_target_obs() throw errors for invalid inputs", {
+test_that("validate_model_oracle_out() throw errors for invalid inputs", {
   expect_error(
-    validate_model_out_target_obs(
+    validate_model_oracle_out(
       model_out_tbl = NULL,
-      target_observations = NULL
+      oracle_output = NULL
     ),
     regexp = "model_out_tbl does not contain required columns: model_id, output_type, output_type_id, value"
   )
   expect_error(
-    suppressMessages(validate_model_out_target_obs(
+    suppressMessages(validate_model_oracle_out(
       model_out_tbl = data.frame(),
-      target_observations = NULL
+      oracle_output = NULL
     )),
     regexp = "model_out_tbl does not contain required columns: model_id, output_type, output_type_id, value"
   )
   expect_error(
-    suppressMessages(validate_model_out_target_obs(
+    suppressMessages(validate_model_oracle_out(
       model_out_tbl = data.frame(),
-      target_observations = data.frame()
+      oracle_output = data.frame()
     )),
     regexp = "model_out_tbl does not contain required columns: model_id, output_type, output_type_id, value"
   )
@@ -27,12 +27,13 @@ test_that("valdiate_model_out_target_obs() works as expected for valid inputs",
   model_out_tbl_1 <- utils::read.csv(
     test_path("testdata/model_out_tbl_point_1.csv")
   )
-  target_observations_1 <- utils::read.csv(
+  oracle_output_1 <- utils::read.csv(
     test_path("testdata/target_data_1.csv")
-  )
-  val_result <- validate_model_out_target_obs(
+  ) |>
+    dplyr::rename(oracle_value = observation)
+  val_result <- validate_model_oracle_out(
     model_out_tbl = model_out_tbl_1,
-    target_observations = target_observations_1
+    oracle_output = oracle_output_1
   )
 
   # expected col names in order
@@ -47,56 +48,58 @@ test_that("valdiate_model_out_target_obs() works as expected for valid inputs",
 })
 
 
-test_that("an error is thrown if target_observations is missing observation column", {
+test_that("an error is thrown if oracle_output is missing oracle_value column", {
   model_out_tbl_1 <- utils::read.csv(
     test_path("testdata/model_out_tbl_point_1.csv")
   )
-  target_observations_1 <- utils::read.csv(
+  oracle_output_1 <- utils::read.csv(
     test_path("testdata/target_data_1.csv")
   ) |>
     dplyr::select(-c("observation"))
   expect_error(
-    suppressMessages(validate_model_out_target_obs(
+    suppressMessages(validate_model_oracle_out(
       model_out_tbl = model_out_tbl_1,
-      target_observations = target_observations_1
+      oracle_output = oracle_output_1
     )),
-    regexp = "target_observations does not have observation column"
+    regexp = "oracle_output does not have oracle_value column"
   )
 })
 
 
-test_that("an error is thrown if model_out_tbl columns do not match target_observations columns", {
+test_that("an error is thrown if model_out_tbl columns do not match oracle_output columns", {
   model_out_tbl_1 <- utils::read.csv(
     test_path("testdata/model_out_tbl_point_1.csv")
   )
-  target_observations_1 <- utils::read.csv(
+  oracle_output_1 <- utils::read.csv(
     test_path("testdata/target_data_1.csv")
-  )
+  ) |>
+    dplyr::rename(oracle_value = observation)
 
   expect_error(
-    suppressMessages(validate_model_out_target_obs(
+    suppressMessages(validate_model_oracle_out(
       model_out_tbl = model_out_tbl_1 |>
         dplyr::rename(loc = location, trgt = target, date = target_end_date),
-      target_observations = target_observations_1
+      oracle_output = oracle_output_1
     )),
-    regexp = "model_out_tbl and target_observations do not have compatible columns"
+    regexp = "model_out_tbl and oracle_output do not have compatible columns"
   )
 
 })
 
 
-test_that("validate_model_out_target_obs() throws error for unexpected columns", {
+test_that("validate_model_oracle_out() throws error for unexpected columns", {
   model_out_tbl_1 <- utils::read.csv(
     test_path("testdata/model_out_tbl_point_1.csv")
   )
-  target_observations_1 <- utils::read.csv(
+  oracle_output_1 <- utils::read.csv(
     test_path("testdata/target_data_1.csv")
-  )
+  ) |>
+    dplyr::rename(oracle_value = observation)
   expect_error(
-    suppressMessages(validate_model_out_target_obs(
+    suppressMessages(validate_model_oracle_out(
       model_out_tbl = model_out_tbl_1 |>
         dplyr::rename(loc = location),
-      target_observations = target_observations_1
+      oracle_output = oracle_output_1
     )),
     regexp = "unexpected column"
   )
diff --git a/tests/testthat/testdata/forecast_outputs.rda b/tests/testthat/testdata/forecast_outputs.rda
deleted file mode 100644
index cc5ceb6..0000000
Binary files a/tests/testthat/testdata/forecast_outputs.rda and /dev/null differ
diff --git a/tests/testthat/testdata/forecast_target_observations.rda b/tests/testthat/testdata/forecast_target_observations.rda
deleted file mode 100644
index 43ee87a..0000000
Binary files a/tests/testthat/testdata/forecast_target_observations.rda and /dev/null differ
diff --git a/tests/testthat/testdata/make_hubExamples_snapshots.R b/tests/testthat/testdata/make_hubExamples_snapshots.R
new file mode 100644
index 0000000..c1ef5a5
--- /dev/null
+++ b/tests/testthat/testdata/make_hubExamples_snapshots.R
@@ -0,0 +1,29 @@
+library(hubExamples)
+
+
+dput_fun <- function(obj, path = stdout()) {
+  x <- get(obj)
+  if (!is.function(x)) {
+    x <- paste(
+      capture.output(dput(x)),
+      # capture.output(dput(x, control = c("keepNA", "keepInteger", "niceNames"))),
+      collapse = "\n  "
+    )
+    res <- glue::glue("# nolint start\n{obj} <- function() {{\n  {dput(x)}\n}}\n# nolint end")
+    writeLines(res, path)
+  }
+}
+
+hubex_forecast_outputs <- hubExamples::forecast_outputs |>
+  as.data.frame()
+# attr(hubex_forecast_outputs, "problems") <- NULL
+hubex_forecast_oracle_output <- hubExamples::forecast_oracle_output |>
+  dplyr::filter(
+    target_end_date >= "2022-10-01", target_end_date <= "2023-02-01",
+    location %in% c("06", "25", "48")
+  ) |>
+  as.data.frame()
+attr(hubex_forecast_oracle_output, "problems") <- NULL
+
+dput_fun("hubex_forecast_outputs", "tests/testthat/helper-hubex_forecast_outputs.R")
+dput_fun("hubex_forecast_oracle_output", "tests/testthat/helper-hubex_forecast_oracle_output.R")
diff --git a/tests/testthat/testdata/make_pmf_test_data.R b/tests/testthat/testdata/make_pmf_test_data.R
index e827e5f..daaf8a7 100644
--- a/tests/testthat/testdata/make_pmf_test_data.R
+++ b/tests/testthat/testdata/make_pmf_test_data.R
@@ -34,13 +34,13 @@ observed_categories <- data.frame(
   stringsAsFactors = FALSE
 )
 
-target_observations <- model_out_tbl |>
+oracle_output <- model_out_tbl |>
   dplyr::distinct(location, target_date, output_type_id) |>
   dplyr::left_join(observed_categories, by = c("location", "target_date", "output_type_id")) |>
-  dplyr::mutate(observation = ifelse(is.na(observation), 0, 1))
+  dplyr::mutate(oracle_value = ifelse(is.na(observation), 0, 1))
 
 # check that observations sum to 1
-target_observations |>
+oracle_output |>
   group_by(location, target_date) |>
   summarize(tot = sum(observation)) |>
   pull(tot)
@@ -77,5 +77,5 @@ dput_fun <- function(obj, path = stdout()) {
   }
 }
 dput_fun("model_out_tbl", "tests/testthat/helper-pmf_test_model_out_tbl.R")
-dput_fun("target_observations", "tests/testthat/helper-pmf_test_target_observations.R")
+dput_fun("oracle_output", "tests/testthat/helper-pmf_test_oracle_output.R")
 dput_fun("exp_forecast", "tests/testthat/helper-pmf_test_expected_merged_forecast.R")