diff --git a/R/convert_output.R b/R/convert_output.R
index e4efb507..58ae9ec7 100644
--- a/R/convert_output.R
+++ b/R/convert_output.R
@@ -553,6 +553,11 @@ convert_output <- function(
                 label = dplyr::case_when(
                   label == "f" ~ "fishing_mortality",
                   TRUE ~ label
+                ),
+                estimate = dplyr::if_else(
+                  grepl("-|_", estimate),
+                  NA,
+                  estimate
                 )
               )
 
@@ -569,12 +574,7 @@ convert_output <- function(
                 )
               colnames(df5) <- tolower(names(df5))
             }
-            # param_df <- df5
-            # if (ncol(out_new) < ncol(df5)){
-            #   warning(paste0("Transformed data frame for ", parm_sel, " has more columns than default."))
-            # } else if (ncol(out_new) > ncol(df5)){
-            #   warning(paste0("Transformed data frame for ", parm_sel, " has less columns than default."))
-            # }
+            
             if ("seas" %in% colnames(df5)) df5 <- dplyr::rename(df5, season = seas)
 
             if ("subseas" %in% colnames(df5)) df5 <- dplyr::rename(df5, subseason = subseas)
diff --git a/R/utils.R b/R/utils.R
index 6ba1da95..1058db87 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -81,10 +81,11 @@ get_ncol <- function(file, skip = 0) {
 
 SS3_extract_df <- function(dat, label) {
   # Convert to data.table if not already
-  if (!inherits(dat, "data.table")) {
-    dat <- data.table::as.data.table(dat)
-  }
+  # if (!inherits(dat, "data.table")) {
+  #   dat <- data.table::as.data.table(dat)
+  # }
 
+  dat <- as.data.frame(dat)
   # Identify all values to be treated as NA
   na_values <- c("", "-", "#")
 
@@ -102,12 +103,27 @@ SS3_extract_df <- function(dat, label) {
 
   # Find the first blank row that appears after the start_row
   end_row <- next_blank_rows[which(next_blank_rows > start_row)[1]]
+  
+  # Add check to determine if after next blank row contains a new report
+  # if not --> then use reference to next report as value
+  # Find all report: rows
+  report_rows <- which(apply(dat, 1, function(row) any(grepl("report:", row))))
+  if (!is.na(end_row) && !any(grepl("report:", dat[end_row + 2, ]))) {
+    # ID where the next report row is
+    end_row <- report_rows[which(report_rows > start_row)[1]]
+    # Go back to the actual end row of target label
+    # Only by one under below condition since in clean_dt we are already doing that
+    if (any(grepl(end_row - 1, next_blank_rows))) end_row <- end_row - 1
+  }
+
   if (is.na(end_row) || length(end_row) == 0) {
     end_row <- nrow(dat)
   }
 
   # Extract the subset
-  clean_dt <- data.table::as.data.table(dat[start_row:(end_row - 1), ])
+  # row_range <- start_row:(end_row - 1)
+  clean_dt <- dat[start_row:(end_row - 1), , drop = FALSE]
+  clean_dt <- data.table::as.data.table(clean_dt)
 
   # Efficiently replace specified values with NA
   for (j in names(clean_dt)) {