globalbioticinteractions/globalbioticinteractions v0.19.2
Bibliographic record
Abstract
Features add taxon mappings for Mammal Species of the World (MSW) support explicit searches for refuting and supporting records (https://github.com/globalbioticinteractions/refuted-biotic-interactions-by-eol/issues/2) support to resolving Plazi taxon concepts, taxonomic treatments and related literature DOIs (https://github.com/globalbioticinteractions/nomer/issues/23 ) Improvements improved handling of unresponsive web resources by reducing timeout (https://github.com/globalbioticinteractions/elton/issues/40) removed verbose "sourceCitation" (dataset citation) property on reference objects, using the primary dataset node citation instead improved logging https://github.com/globalbioticinteractions/globalbioticinteractions/issues/532 removed under-used ecoregion search index (https://github.com/globalbioticinteractions/globalbioticinteractions/issues/473) add catalogNumber, collectionCode and institution code mappings for DwC-ish exports (#529) upgrade to GloBI taxon graph v0.3.26 (https://github.com/globalbioticinteractions/nomer/issues/23) improved indexing of Pensoft tables via https://github.com/pensoft/pensoft-interaction-tables and OpenBiodiv (https://github.com/pensoft/pensoft-interaction-tables/issues/11#issuecomment-707591310 https://github.com/globalbioticinteractions/globalbioticinteractions/issues/481 https://github.com/globalbioticinteractions/globalbioticinteractions/issues/526 https://github.com/globalbioticinteractions/globalbioticinteractions/issues/524) Bug fixes improve DwC ISO8601 datetime range handling (https://github.com/globalbioticinteractions/globalbioticinteractions/issues/535)
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.240 | 0.397 |
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".