diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 4ca08d935..350fe0b5c 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -122,6 +122,27 @@ Notes:
    - **PR labels**: Add labels with the same names (e.g., `skip:ci`, `skip:test:long-running`) to your pull request
    - Both methods work independently - you can use either or both
 
+
+### Run Github Actions
+
+One can trigger GitHub Actions workflows locally using commands like so:
+
+```shell
+BRANCH=$(git rev-parse --abbrev-ref HEAD)
+
+# Hourly (quick smoke) and Daily (thorough) against staging
+gh workflow run scheduled-testing-staging-hourly.yml    --ref "$BRANCH" -f branch="$BRANCH"
+gh workflow run scheduled-testing-staging-daily.yml     --ref "$BRANCH" -f branch="$BRANCH"
+
+# Same against production
+gh workflow run scheduled-testing-production-hourly.yml --ref "$BRANCH" -f branch="$BRANCH"
+gh workflow run scheduled-testing-production-daily.yml  --ref "$BRANCH" -f branch="$BRANCH"
+```
+
+This is especially useful for testing that new he-tme versions will pass the scheduled testing workflows before merging into main.
+
+
+
 ### Publish Release
 
 Releases follow a four-phase workflow that allows Ketryx compliance approvals to be collected before publishing:
diff --git a/tests/constants_test.py b/tests/constants_test.py
index 3fc1c2d35..5df88ab74 100644
--- a/tests/constants_test.py
+++ b/tests/constants_test.py
@@ -74,45 +74,45 @@
 # nearly-identical blocks as duplicated code (the 3% duplication threshold).
 SPOT_0_EXPECTED_RESULT_FILES = [
     ("tissue_qc_csv_class_information.csv", 285, 10),
-    ("tissue_qc_geojson_polygons.json", 101150, 10),
-    ("tissue_qc_parquet_polygons.parquet", 39435, 10),
-    ("tissue_qc_segmentation_map_image.tiff", 1645652, 10),
+    ("tissue_qc_geojson_polygons.json", 100956, 10),
+    ("tissue_qc_parquet_polygons.parquet", 39384, 10),
+    ("tissue_qc_segmentation_map_image.tiff", 1645632, 10),
     ("tissue_segmentation_csv_class_information.csv", 452, 10),
-    ("tissue_segmentation_geojson_polygons.json", 327625, 10),
-    ("tissue_segmentation_parquet_polygons.parquet", 117509, 10),
-    ("tissue_segmentation_segmentation_map_image.tiff", 2858496, 10),
-    ("cell_classification_geojson_polygons.json", 6117357, 10),
-    ("cell_classification_parquet_polygons.parquet", 1985592, 10),
-    ("readout_generation_cell_readouts.csv", 1660865, 10),
-    ("readout_generation_slide_readouts.csv", 303585, 10),
+    ("tissue_segmentation_geojson_polygons.json", 396960, 10),
+    ("tissue_segmentation_parquet_polygons.parquet", 145744, 10),
+    ("tissue_segmentation_segmentation_map_image.tiff", 3078034, 10),
+    ("cell_classification_geojson_polygons.json", 6385805, 10),
+    ("cell_classification_parquet_polygons.parquet", 1998420, 10),
+    ("readout_generation_cell_readouts.csv", 1654453, 10),
+    ("readout_generation_slide_readouts.csv", 303708, 10),
 ]
 SPOT_0_EXPECTED_CELLS_CLASSIFIED = (39798, 10)
 
 SPOT_1_EXPECTED_RESULT_FILES = [
     ("tissue_qc_csv_class_information.csv", 290, 10),
     ("tissue_qc_geojson_polygons.json", 75281, 10),
-    ("tissue_qc_parquet_polygons.parquet", 29087, 10),
+    ("tissue_qc_parquet_polygons.parquet", 29075, 10),
     ("tissue_qc_segmentation_map_image.tiff", 1288632, 10),
-    ("tissue_segmentation_csv_class_information.csv", 446, 10),
-    ("tissue_segmentation_geojson_polygons.json", 152301, 10),
-    ("tissue_segmentation_parquet_polygons.parquet", 56563, 10),
-    ("tissue_segmentation_segmentation_map_image.tiff", 1783376, 10),
-    ("cell_classification_geojson_polygons.json", 1726813, 10),
-    ("cell_classification_parquet_polygons.parquet", 562536, 10),
-    ("readout_generation_cell_readouts.csv", 464838, 10),
-    ("readout_generation_slide_readouts.csv", 299361, 10),
+    ("tissue_segmentation_csv_class_information.csv", 444, 10),
+    ("tissue_segmentation_geojson_polygons.json", 163318, 10),
+    ("tissue_segmentation_parquet_polygons.parquet", 60980, 10),
+    ("tissue_segmentation_segmentation_map_image.tiff", 1806464, 10),
+    ("cell_classification_geojson_polygons.json", 1727805, 10),
+    ("cell_classification_parquet_polygons.parquet", 562586, 10),
+    ("readout_generation_cell_readouts.csv", 465593, 10),
+    ("readout_generation_slide_readouts.csv", 299708, 10),
 ]
 
 match os.getenv("AIGNOSTICS_PLATFORM_ENVIRONMENT", "production"):
     case "production":
         TEST_APPLICATION_ID = "test-app"
-        TEST_APPLICATION_VERSION = "1.0.0"
+        TEST_APPLICATION_VERSION = "1.3.0"
 
         HETA_APPLICATION_ID = "he-tme"
-        HETA_APPLICATION_VERSION = "1.2.0"
-        TEST_APPLICATION_VERSION_USE_LATEST_FALLBACK_SKIP = False
+        HETA_APPLICATION_VERSION = "1.3.0"
+        TEST_APPLICATION_VERSION_USE_LATEST_FALLBACK_SKIP = True
 
-        PIPELINE_GPU_TYPE = "L4"
+        PIPELINE_GPU_TYPE = "A100"
         PIPELINE_GPU_PROVISIONING_MODE = "SPOT"
         PIPELINE_GPU_FLEX_START_MAX_RUN_DURATION_MINUTES = None
         PIPELINE_MAX_GPUS_PER_SLIDE = 1
@@ -121,13 +121,13 @@
 
     case "staging":
         TEST_APPLICATION_ID = "test-app"
-        TEST_APPLICATION_VERSION = "1.0.0"
+        TEST_APPLICATION_VERSION = "1.3.0"
 
         HETA_APPLICATION_ID = "he-tme"
-        HETA_APPLICATION_VERSION = "1.3.0-rc.1"
+        HETA_APPLICATION_VERSION = "1.3.0"
         TEST_APPLICATION_VERSION_USE_LATEST_FALLBACK_SKIP = True
 
-        PIPELINE_GPU_TYPE = "L4"
+        PIPELINE_GPU_TYPE = "A100"
         PIPELINE_GPU_PROVISIONING_MODE = "SPOT"
         PIPELINE_GPU_FLEX_START_MAX_RUN_DURATION_MINUTES = None
         PIPELINE_MAX_GPUS_PER_SLIDE = 1