diff --git a/tests/utils_test/uncertainty_metrics_test.py b/tests/utils_test/uncertainty_metrics_test.py
index 810006e8..5d1abbea 100644
--- a/tests/utils_test/uncertainty_metrics_test.py
+++ b/tests/utils_test/uncertainty_metrics_test.py
@@ -1,5 +1,5 @@
 import functools
-from typing import Tuple
+from typing import Callable, Tuple
 
 import jax
 import jax.numpy as jnp
@@ -32,15 +32,15 @@ def test_uncertainty_metrics() -> None:
     random_key = jax.random.PRNGKey(seed)
 
     # First, init a deterministic environment
-    scoring_fn = arm_scoring_function
+    genotype_dim = 8
 
     # Init policies
     init_policies = jax.random.uniform(
-        random_key, shape=(batch_size, 8), minval=0, maxval=1
+        random_key, shape=(batch_size, genotype_dim), minval=0, maxval=1
     )
 
     # Evaluate in the deterministic environment
-    fitnesses, descriptors, extra_scores, random_key = scoring_fn(
+    fitnesses, descriptors, extra_scores, random_key = arm_scoring_function(
         init_policies, random_key
     )
 
@@ -75,7 +75,7 @@ def test_uncertainty_metrics() -> None:
     corrected_repertoire, random_key = reevaluation_function(
         repertoire=repertoire,
         empty_corrected_repertoire=empty_corrected_repertoire,
-        scoring_fn=scoring_fn,
+        scoring_fn=arm_scoring_function,
         num_reevals=num_reevals,
         random_key=random_key,
     )
@@ -94,7 +94,7 @@ def test_uncertainty_metrics() -> None:
     ) = reevaluation_reproducibility_function(
         repertoire=repertoire,
         empty_corrected_repertoire=empty_corrected_repertoire,
-        scoring_fn=scoring_fn,
+        scoring_fn=arm_scoring_function,
         num_reevals=num_reevals,
         random_key=random_key,
     )
@@ -178,7 +178,7 @@ def play_step_fn(
 
     # Create the scoring function
     bd_extraction_fn = environments.behavior_descriptor_extractor[env_name]
-    scoring_fn = functools.partial(
+    brax_scoring_fn: Callable = functools.partial(
         scoring_function_brax_envs,
         init_states=init_states,
         episode_length=episode_length,
@@ -187,7 +187,7 @@ def play_step_fn(
     )
 
     # Evaluate in the Brax environment
-    fitnesses, descriptors, extra_scores, random_key = scoring_fn(
+    fitnesses, descriptors, extra_scores, random_key = brax_scoring_fn(
         init_policies, random_key
     )
 
@@ -220,11 +220,9 @@ def play_step_fn(
     )
 
     # Test that reevaluation_function runs and keeps at least one solution
-    keys = jnp.repeat(
-        jnp.expand_dims(subkey, axis=0), repeats=num_centroids, axis=0
-    )
+    keys = jnp.repeat(jnp.expand_dims(subkey, axis=0), repeats=num_centroids, axis=0)
     init_states = reset_fn(keys)
-    reeval_scoring_fn = functools.partial(
+    reeval_brax_scoring_fn: Callable = functools.partial(
         scoring_function_brax_envs,
         init_states=init_states,
         episode_length=episode_length,
@@ -234,7 +232,7 @@ def play_step_fn(
     corrected_repertoire, random_key = reevaluation_function(
         repertoire=repertoire,
         empty_corrected_repertoire=empty_corrected_repertoire,
-        scoring_fn=reeval_scoring_fn,
+        scoring_fn=reeval_brax_scoring_fn,
         num_reevals=num_reevals,
         random_key=random_key,
     )