Added more explainers

about.py

@@ -42,7 +42,9 @@ Here we invite the community to submit and develop better predictors, which will
 #### 🏆 Prizes
 
 For each of the 5 properties in the competition, there is a prize for the model with the highest performance for that property on the private test set.
-There is also an 'open-source' prize for the best model trained on the GDPa1 dataset of monoclonal antibodies (reporting cross-validation results) and assessed on the private test set where authors provide all training code and data.
+There is also an 'open-source' prize for the best reproducible model: one that is trained on the GDPa1 dataset (reporting cross-validation results) and assessed on the private test set where authors provide all training code and data. 
+This will be judged by a panel (i.e. by default the model with the highest average Spearman correlation across all properties will be selected, but a really good model on just one property may be better for the community).
+
 For each of these 6 prizes, participants have the choice between
 - **$10 000 in data generation credits** with [Ginkgo Datapoints](https://datapoints.ginkgo.bio/), or
 - A **$2000 cash prize**.
@@ -124,7 +126,7 @@ FAQS = {
         "No, there are no requirements to submit code / methods and submitted predictions remain private. "
         "We also have an optional field for including a short model description. "
         "Top performing participants will be requested to identify themselves at the end of the tournament. "
-        "There will be one prize for the best open-source model, which will require code / methods to be available."
+        "There will be one prize for the best open-source reproducible model, which will require code / methods to be available."
     ),
     "How exactly can I evaluate my model?": (
         "You can easily calculate the Spearman correlation coefficient on the GDPa1 dataset yourself before uploading to the leaderboard. "
@@ -172,25 +174,28 @@ SUBMIT_INSTRUCTIONS = f"""
 You do **not** need to predict all 5 properties — each property has its own leaderboard and prize.
 
 ## Instructions
-1. **Upload both CSV files**:
-   - **GDPa1 Cross-Validation predictions** (using cross-validation folds)
-   - **Private Test Set predictions** (final test submission)
+1. **Upload two CSV files**: one with GDPa1 cross-validation predictions, and one with private test set predictions
 2. Each CSV should contain `antibody_name` + one column per property you are predicting (e.g. `"antibody_name,Titer,PR_CHO"` if your model predicts Titer and Polyreactivity).
    - List of valid property names: `{', '.join(ASSAY_LIST)}`.
-3. Submit as many times as you like, and the latest submission will be used for the leaderboard (and test set scoring at the end of the competition).
+   - Include the `"hierarchical_cluster_IgG_isotype_stratified_fold"` column if submitting cross-validation predictions.
+3. You can resubmit as often as you like; only your latest submission will count for both the leaderboard and final test set scoring.
 
 The GDPa1 results should appear on the leaderboard within a minute, and can also be calculated manually using average Spearman rank correlation across the 5 folds. 
 
 ## Cross-validation
 
-For the GDPa1 cross-validation predictions, use the `"hierarchical_cluster_IgG_isotype_stratified_fold"` column to split the dataset into folds and make predictions for each of the folds.
-Submit a CSV file in the same format but also containing the `"hierarchical_cluster_IgG_isotype_stratified_fold"` column.
-Check out our tutorial on training an antibody developability prediction model with cross-validation [here]({TUTORIAL_URL}).
+For the GDPa1 cross-validation predictions:
+1. Split the dataset using the `"hierarchical_cluster_IgG_isotype_stratified_fold"` column
+2. Train on 4 folds and predict on the held-out fold
+3. Collect held-out predictions for all 5 folds into one dataframe
+4. Write this dataframe to a .csv file and submit as your GDPa1 cross-validation predictions
+
+The leaderboard will show the average Spearman rank correlation across the 5 folds. For a code example, check out our tutorial on training an antibody developability prediction model with cross-validation [here]({TUTORIAL_URL}).
 
 ## Test set
 
-The **private test set results will not appear on the leaderboards at first**, and will be used to determine the winners at the close of the competition.
-🗓️ There will be a test set scoring on **October 13th** (which will score all the latest test set submissions at that point).
+The **private test set submissions will not be scored automatically**, to avoid test set hacking. They will be evaluated after submissions close to determine winners.
+🗓️ We will release one interim scoring of the latest private test set submissions on **October 13th**. Use this opportunity to see how your model is performing on the heldout test set and refine accordingly.
 
 Submissions close on **1 November 2025**.
 """