globalbioticinteractions/globalbioticinteractions: v0.24.6
Bibliographic record
Abstract
Features n/a Improvements more improvement to reduce time to discover locally indexed datasets (https://github.com/globalbioticinteractions/globalbioticinteractions/issues/834) add support for Catalogue of Life integration via wikidata https://github.com/globalbioticinteractions/nomer/issues/102 https://github.com/CatalogueOfLife/general/issues/94 @Daniel-Mietchen add support for World of Flora Online integration via Wikidata https://github.com/globalbioticinteractions/globalbioticinteractions/commit/fb1d338374313cfd4b08cd871fa71f422ab4e154 @Daniel-Mietchen make templates for wikidata sparql queries https://github.com/globalbioticinteractions/globalbioticinteractions/issues/835 suggested by @Daniel-Mietchen add support for exporting interactions.tsv/csv without taxonomic name interpretation as verbatim-interactions.tsv/csv and refuted-verbatim-interactions.tsv/csv https://github.com/globalbioticinteractions/globalbioticinteractions/issues/826 @BarbaraMeulenbelt @seltmann Bug fixes allow for taxonCache/taxonMap to be resolved via non-local resource services (https://github.com/globalbioticinteractions/nomer/issues/125) reduce disable mapdb "hack" that accessed non-public accessors programmatically https://github.com/globalbioticinteractions/globalbioticinteractions/commit/c4721ef7fb0875439fed69231f5c3c989d1e766e
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.257 | 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".