Something Old, Something New: Identifying Knowledge Source in Bio-Events
Bibliographic record
Abstract
Locating new experimental knowledge in biomedical texts is important for several tasks undertaken by biologists. Although several systems can distinguish between new and existing knowledge, this generally happens at the text zone level. In contrast to text zones, bio-events constitute structured representations of biomedical knowledge. They bridge text with domain knowledge and can be used to develop sophisticated semantic search systems. Typically, event extraction systems locate and classify events and their arguments, but ignore interpretative information (meta-knowledge) from their textual context. Since several events (often nested) can occur in a sentence, determining which event(s) are affected by which textual clues can be complex. We have analysed knowledge source annotation in two bio-event corpora: GENIA-MK (abstracts) and FP-MK (full papers), and have developed a system to classify bioevents automatically according to their knowledge source. Our system performs with an accuracy of over 99% on both abstracts and full papers.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".