Bibliographic record
Abstract
This describes the output files for the BioWordlists project. These files are ancillary data for other text mining projects. Each file is a tab-delimited file with one term per line. The first column is a unique ID. The second column is the main name of the term. The third column is a pipe-delimited set of the synonyms for this term (including the main term). terms_genes.tsv: This is a list of all human genes with synonyms. The first column is the HUGO gene ID. It includes an additional fourth column is the Entrez gene ID. Genes are built using the NCBI Gene resource with synonyms from the UMLS Metathesaurus. terms_drugs.tsv: This is a list of all drugs from the WikiData resource. It also includes some more general terms and inhibitors terms for all genes in the gene list. terms_cancers.tsv: This is a list of specific cancer types from the Disease Ontology. General cancer terms have been removed and synonyms added from the UMLS Metathesaurus. terms_variants.tsv: Common mutations, aberrations and other 'omic events that may occur to a gene, especially in the cancer setting. terms_conflicting.tsv: Several common biomedical terms that are easily confused with other useful concepts. An examples is "Cox Regression". This list is used to identify these to reduce ambiguity. terms_proteins.tsv: Human protein names from UniProt with synonyms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.584 | 0.602 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".