Remove limitations section from dataset card

README.md

@@ -117,15 +117,6 @@ gold_label = row["trustworthy"]  # For supervision or scoring only.
 
 Treat `true` as the positive class. Report accuracy, macro-F1, per-class precision and recall, and invalid/missing prediction counts. Source-specific results help distinguish performance on naturally occurring untrustworthy comments from performance on perturbations; include sample counts for each slice.
 
-## Limitations
-
-- Coverage is concentrated in a small number of repositories and in Python. The validation split contains only 8 PRs, only Python examples, and only 6 naturally occurring untrustworthy comments.
-- Perturbations account for 370 of 435 negative examples. Performance may reflect sensitivity to these edits and may not generalize to the full range of naturally occurring review errors.
-- Multiple examples share PR context, and original comments can have perturbed counterparts. Examples are therefore correlated; PR-level grouping matters for both splitting and uncertainty estimates.
-- Labels are task-specific judgments. The release does not establish inter-annotator agreement or guarantee error-free annotations.
-- Public GitHub content may already occur in model pretraining data. Pretraining contamination has not been measured.
-- GitHub usernames and discussion text are retained. Modified comments should not be attributed to the original reviewers as statements they actually made.
-
 ## License and attribution
 
 No dataset-wide license is specified for this release. This card does not assign a new license to the collected code or discussions. Source repositories are identified in the `repo` field, and PR numbers are available in `pull_request.pull_number`.