Thematic mapping: metadata enrichment, keyword cleanup, and area-label improvements - #882
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…ted; including tests
…lence of new ngram generation
…lence of new ngram generation
…classification parsing improvements; TeX quotation cleanup
…apping-polishing-4 Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Thematic mapping: metadata enrichment, keyword cleanup, and area-label improvements
This branch brings together the thematic-mapping work. It has 221 commits across 371 files. About 80% of the added lines are test replay fixtures and
contentprovider.jsoncache updates.1. Deduplication and metadata enrichment (BASE, ORCID)
is_anchorflag, and anchor selection and ordering are now deterministic. Before this, ties on BASE's unstable response order could produce different results from run to run.additional_doifallback is used for enrichment, and SOLR escaping for DOIs with special characters is fixed.2. Keyword and classification cleanup (R preprocessing)
subject_cleaning.R) replaces the chainedgsubcalls.3. Area-label generation
RANKING_MODE[_<SERVICE>]. Mode 0 is the default and reproduces the legacy output exactly.NGRAM_SETTING[_<SERVICE>]andINCLUDE_ABSTRACTS. Setting 0 is the legacy baseline.4. Tests and tooling
5. Configuration and ops
docker-compose.ymlpasses the new ranking and n-gram environment variables through. They are documented inlocal_dev/dev.env.example.ENRICHMENT_STRATEGY_FOR_SUBJECTis removed.LOGLEVELinstead ofLOG_LEVEL. Check the deployment environment.flask-restxis pinned for Python 3.9, and the renv build order is fixed.