Update README, metadata and performance tables

README.md

@@ -72,7 +72,7 @@ Repository-level files:
 | `all_count_pearson`, `all_count_spearman`, `all_count_mse` | peaks and background regions | the same count metrics over both kinds of region |
 | `auroc`, `auprc` | peaks and background regions | separation of peaks from background regions by the predicted log count |
 
-The headline metrics are `profile_pearson` (profile shape) and `count_pearson` (binding strength across peaks). `n_peaks` is the number of distinct peak regions in that fold's validation or test peak file, before exclusion-list filtering. Some experiments have few peaks, so their metrics are noisy: 142 of the 17,145 folds have fewer than 10 validation peaks and 31 have fewer than 10 test peaks.
+The headline metrics are `profile_pearson` (profile shape) and `count_pearson` (binding strength across peaks). `n_peaks` is the number of distinct peak regions in that fold's validation or test peak file, before exclusion-list filtering. Some experiments have few peaks, so their metrics are noisy: 142 of the 17,145 folds have fewer than 10 validation peaks and 31 have fewer than 10 test peaks. A low score on a fold with few test peaks therefore does not by itself show that the model is poor. The 506 folds with fewer than 100 test peaks (3.0% of all folds) hold 44 of the 90 folds whose test `profile_pearson` or `count_pearson` is at or below 0, and in experiments with such a fold the standard deviation of test `count_pearson` across folds has a median of 0.126, against 0.023 in the others. Because these experiments also train on few peaks, some of their models are weaker as well; compare an experiment's five folds, and its validation and test scores, before judging it.
 
 ### Metadata