diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index 2e6be81..fca119d 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -2,7 +2,7 @@
 #' each combination of model task, output type, and output type id. Supported
 #' output types include `mean`, `median`, `quantile`, `cdf`, and `pmf`.
 #'
-#' @param model_outputs an object of class `model_output_df` with component
+#' @param model_outputs an object of class `model_out_tbl` with component
 #'   model outputs (e.g., predictions).
 #' @param weights an optional `data.frame` with component model weights. If
 #'   provided, it should have a column named `model_id` and a column containing
@@ -24,10 +24,6 @@
 #'   case all columns in `model_outputs` other than `"model_id"`, the specified
 #'   `output_type_col` and `output_type_id_col`, and `"value"` are used as task
 #'   ids.
-#' @param output_type_col `character` string with the name of the column in
-#'   `model_outputs` that contains the output type.
-#' @param output_type_id_col `character` string with the name of the column in
-#'   `model_outputs` that contains the output type id.
 #'
 #' @details The default for `agg_fun` is `"mean"`, in which case the ensemble's
 #'   output is the average of the component model outputs within each group
@@ -40,8 +36,7 @@
 #'   `agg_fun = "median"` are translated to use `matrixStats::weightedMean` and
 #'   `matrixStats::weightedMedian` respectively.
 #'
-#' @return a data.frame with columns `model_id`, one column for
-#'   each task id variable, `output_type`, `output_id`, and `value`. Note that
+#' @return a `model_out_tbl` object of ensemble predictions. Note that
 #'   any additional columns in the input `model_outputs` are dropped.
 #'
 #' @export
@@ -49,37 +44,35 @@ simple_ensemble <- function(model_outputs, weights = NULL,
                             weights_col_name = "weight",
                             agg_fun = "mean", agg_args = list(),
                             model_id = "hub-ensemble",
-                            task_id_cols = NULL,
-                            output_type_col = "output_type",
-                            output_type_id_col = "output_type_id") {
+                            task_id_cols = NULL) {
   if (!is.data.frame(model_outputs)) {
     cli::cli_abort(c("x" = "{.arg model_outputs} must be a `data.frame`."))
   }
 
+  if (isFALSE("model_out_tbl" %in% class(model_outputs))) {
+    model_outputs <- hubUtils::as_model_out_tbl(model_outputs)
+  }
+
   model_out_cols <- colnames(model_outputs)
 
-  non_task_cols <- c("model_id", output_type_col, output_type_id_col, "value")
+  non_task_cols <- c("model_id", "output_type", "output_type_id", "value")
   if (is.null(task_id_cols)) {
     task_id_cols <- model_out_cols[!model_out_cols %in% non_task_cols]
   }
 
-  req_col_names <- c(non_task_cols, task_id_cols)
-  if (!all(req_col_names %in% model_out_cols)) {
+  if (!all(task_id_cols %in% model_out_cols)) {
     cli::cli_abort(c(
-      "x" = "{.arg model_outputs} did not have all required columns
-             {.val {req_col_names}}."
+      "x" = "{.arg model_outputs} did not have all listed task id columns
+             {.val {task_id_col}}."
     ))
   }
-
-  ## Validations above this point to be relocated to hubUtils
-  # hubUtils::validate_model_output_df(model_outputs)
-
-  if (nrow(model_outputs) == 0) {
-    cli::cli_warn(c("!" = "{.arg model_outputs} has zero rows."))
-  }
+  
+  # check `model_outputs` has all standard columns with correct data type
+  # and `model_outputs` has > 0 rows
+  hubUtils::validate_model_out_tbl(model_outputs)
 
   valid_types <- c("mean", "median", "quantile", "cdf", "pmf")
-  unique_types <- unique(model_outputs[[output_type_col]])
+  unique_types <- unique(model_outputs[["output_type"]])
   invalid_types <- unique_types[!unique_types %in% valid_types]
   if (length(invalid_types) > 0) {
     cli::cli_abort(c(
@@ -140,14 +133,13 @@ simple_ensemble <- function(model_outputs, weights = NULL,
                                  w = quote(.data[[weights_col_name]])))
   }
 
-  group_by_cols <- c(task_id_cols, output_type_col, output_type_id_col)
+  group_by_cols <- c(task_id_cols, "output_type", "output_type_id")
   ensemble_model_outputs <- model_outputs %>%
     dplyr::group_by(dplyr::across(dplyr::all_of(group_by_cols))) %>%
     dplyr::summarize(value = do.call(agg_fun, args = agg_args)) %>%
     dplyr::mutate(model_id = model_id, .before = 1) %>%
-    dplyr::ungroup()
-
-  # hubUtils::as_model_output_df(ensemble_model_outputs)
+    dplyr::ungroup() %>%
+    hubUtils::as_model_out_tbl()
 
   return(ensemble_model_outputs)
 }
diff --git a/man/simple_ensemble.Rd b/man/simple_ensemble.Rd
index 8dfbed3..3f514cd 100644
--- a/man/simple_ensemble.Rd
+++ b/man/simple_ensemble.Rd
@@ -13,13 +13,11 @@ simple_ensemble(
   agg_fun = "mean",
   agg_args = list(),
   model_id = "hub-ensemble",
-  task_id_cols = NULL,
-  output_type_col = "output_type",
-  output_type_id_col = "output_type_id"
+  task_id_cols = NULL
 )
 }
 \arguments{
-\item{model_outputs}{an object of class \code{model_output_df} with component
+\item{model_outputs}{an object of class \code{model_out_tbl} with component
 model outputs (e.g., predictions).}
 
 \item{weights}{an optional \code{data.frame} with component model weights. If
@@ -47,16 +45,9 @@ ensemble model.}
 case all columns in \code{model_outputs} other than \code{"model_id"}, the specified
 \code{output_type_col} and \code{output_type_id_col}, and \code{"value"} are used as task
 ids.}
-
-\item{output_type_col}{\code{character} string with the name of the column in
-\code{model_outputs} that contains the output type.}
-
-\item{output_type_id_col}{\code{character} string with the name of the column in
-\code{model_outputs} that contains the output type id.}
 }
 \value{
-a data.frame with columns \code{model_id}, one column for
-each task id variable, \code{output_type}, \code{output_id}, and \code{value}. Note that
+a \code{model_out_tbl} object of ensemble predictions. Note that
 any additional columns in the input \code{model_outputs} are dropped.
 }
 \description{