Change repro code availability for most deep learning and statistical models to No

results/DLinear/config.json

@@ -4,5 +4,5 @@
     "model_dtype": "float32",
     "org": "The Chinese University of Hong Kong",
     "testdata_leakage": "No",
-    "replication_code_available": "Yes"
+    "replication_code_available": "No"
 }

results/N-BEATS/config.json

@@ -4,5 +4,5 @@
     "model_dtype": "float32",
     "org": "ServiceNow",
     "testdata_leakage": "No",
-    "replication_code_available": "Yes"
+    "replication_code_available": "No"
 }

results/PatchTST/config.json

@@ -4,5 +4,5 @@
     "model_dtype": "float32",
     "org": "Princeton University",
     "testdata_leakage": "No",
-    "replication_code_available": "Yes"
+    "replication_code_available": "No"
 }

results/auto_arima/config.json

@@ -3,5 +3,5 @@
     "model_type": "statistical",
     "model_dtype": "float32",
     "testdata_leakage": "No",
-    "replication_code_available": "Yes"
+    "replication_code_available": "No"
 }

results/auto_ets/config.json

@@ -3,5 +3,5 @@
     "model_type": "statistical",
     "model_dtype": "float32",
     "testdata_leakage": "No",
-    "replication_code_available": "Yes"
+    "replication_code_available": "No"
 }

results/auto_theta/config.json

@@ -3,5 +3,5 @@
     "model_type": "statistical",
     "model_dtype": "float32",
     "testdata_leakage": "No",
-    "replication_code_available": "Yes"
-}
+    "replication_code_available": "No"
+}

results/crossformer/config.json

@@ -4,5 +4,5 @@
     "model_dtype": "float32",
     "org": "Shanghai Jiao Tong University",
     "testdata_leakage": "No",
-    "replication_code_available": "Yes"
+    "replication_code_available": "No"
 }

results/deepar/config.json

@@ -4,5 +4,5 @@
     "model_dtype": "float32",
     "org": "Amazon Research",
     "testdata_leakage": "No",
-    "replication_code_available": "Yes"
+    "replication_code_available": "No"
 }

results/tft/config.json

@@ -4,5 +4,5 @@
     "model_dtype": "float32",
     "org": "Google Research",
     "testdata_leakage": "No",
-    "replication_code_available": "Yes"
+    "replication_code_available": "No"
 }

results/tide/config.json

@@ -4,5 +4,5 @@
     "model_dtype": "float32",
     "org": "Google Research",
     "testdata_leakage": "No",
-    "replication_code_available": "Yes"
+    "replication_code_available": "No"
 }

src/about.py

@@ -44,6 +44,9 @@ points, spanning seven domains, 10 frequencies, multivariate inputs, and predict
 LLM_BENCHMARKS_TEXT = f"""
 ## Update Log
 
+### 2025-10-17
+- Added new column: Repro. Code to indicate whether the model's evaluation code is made available. This column is a binary indicator specifying whether the model's evaluation code is made available to the public by the submission author. The preferable way to share the evaluation code is to share a notebook in the GIFT-Eval github repository (as many previous submissions have done), but a standalone repo for the evaluation code is also acceptable as long as it is accessible to the public and the link is provided in the config.json file.
+
 ### 2025-08-25
 - Added new model type: Zero-shot to distinguish between foundation model submissions that don't use training data of GIFT-Eval. Now models tagged with zero-shot indicate that the model is not trained on the GIFT-Eval training data. Test data leakage is still separately tracked with the TestData Leakage column. For a model be tagged as `zero-shot`, it must both not have test data leakage and not use any training split from GIFT-Eval.