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Implements solution for issue #37: Automated checking of bibliographic entries against external sources to verify accuracy of metadata. New features: - bibverify.py: Python script to verify .bib entries against CrossRef API - Parallel batch processing for efficient verification of large bibliographies - DOI-based and title-based lookup strategies with fuzzy matching - Comprehensive verification of titles, authors, years, journals, volumes, pages - Detailed discrepancy reporting with suggestions for corrections - Thread-safe parallel processing with configurable worker count - BIBVERIFY_README.md: Complete documentation and usage guide Technical details: - Uses CrossRef REST API (170M+ records, free, unlimited) - Supports 1-20 parallel workers for scalable performance - Smart fuzzy matching with configurable similarity thresholds - Respects API rate limits with built-in delays - Framework in place for future auto-fix functionality Performance: Can verify 6,151 entries in ~30-60 minutes with 10 workers (compared to 5-8 hours sequentially) Related to: #37
Test results show: - Processing speed: 17.5 entries/second with 10 workers - Full bibliography (6,151 entries) estimated at ~6 minutes - Found discrepancies in 78% of tested entries - Common issues: missing DOIs, formatting differences, some genuine errors Performance demonstrates that automated verification is highly feasible and practical for the CDL bibliography at scale.
Major improvements based on user feedback: 1. Conservative Match Verification: - Requires ALL of: title ≥85%, authors ≥70%, journal ≥60%, year ≤1 difference - Rejects uncertain matches rather than reporting false positives - Fixes GuoEtal20 false positive (0% author match correctly rejected) 2. Focus on Metadata Accuracy: - Only verifies volume/pages/number when confident match found - Removed DOI suggestions (not needed per formatting guide) - Detects common errors (DOI in pages field) 3. Results Improvement: - 54% verified (vs 2% before) - 14% real errors (vs 78% false positives before) - No more wrong-paper suggestions Test results on 100 entries: - Real errors found: volume mismatches, DOI in pages, year discrepancies - False positives eliminated: GuoEtal20, etc. - Processing speed: ~12 entries/sec with conservative matching Addresses feedback on issue #37.
…ication results Changes: - Integrated bibverify.py documentation into main README.md - Removed standalone BIBVERIFY_README.md (now in README.md) - Removed VERIFICATION_TEST_RESULTS.md (superseded by full report) - Added full_verification_report.txt with complete verification results Full verification results (6,151 entries in 6min 11sec): - ✓ Verified: 3,988 entries (65%) - ✗ Errors: 724 entries (12%) - real metadata issues - ⚠ Warnings: 1,434 entries (23%) - not in CrossRef or uncertain match Common errors found: - Volume/issue number mismatches - Page range errors (off-by-one) - DOI in pages field instead of doi field - Year discrepancies (preprint vs published) The bibverify tool successfully demonstrates feasibility of automated bibliographic accuracy verification at scale, addressing issue #37.
Removed per user request - full report not needed in repository.
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auto verification