diff --git a/DESCRIPTION b/DESCRIPTION
index 322f390..e62abf1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -22,7 +22,7 @@ Imports:
     qs2,
     R6,
     Rcpp,
-    terra
+    terra (>=1.8-42)
 Suggests:
     base64enc,
     butcher,
diff --git a/inst/tinytest/test_integ_allocation.R b/inst/tinytest/test_integ_allocation.R
index 06884d7..60e9fc1 100644
--- a/inst/tinytest/test_integ_allocation.R
+++ b/inst/tinytest/test_integ_allocation.R
@@ -49,6 +49,8 @@ if (Sys.which("DinamicaConsole") == "") {
   expect_stdout(
     db$alloc_dinamica(
       id_periods = 4,
+      gof_criterion = "auc",
+      gof_maximize = TRUE,
       work_dir = file.path(db$path, "dinamica_test"),
       keep_intermediate = FALSE
     ),
diff --git a/vignettes/evoland.qmd b/vignettes/evoland.qmd
index ea6121e..bd005e0 100644
--- a/vignettes/evoland.qmd
+++ b/vignettes/evoland.qmd
@@ -370,7 +370,7 @@ db$trans_preds_t <- db$get_pruned_trans_preds_t(
 
 Now we fit partial models using training/validation splits, allowing for a goodness-of-fit (gof) estimation.
 Here, we only fit a single partial model per transition, where we normally would create a list of candidates.
-We can then pick the models with the best goodness of fit to retrain full models on all of the available predictor data; the full models are then used during extrapolation.
+Next, fit_full_models() reads the partial models stored in db$trans_models_t, chooses the best model for each transition based on goodness of fit, and refits each chosen model on all available predictor data. Assigning the result back to db$trans_models_t stores these full models for the extrapolation step.
 
 ```{r}
 #| label: trans-models