Bibliographic record
Abstract
A dataset listing the 18 species recorded in GBIF matching the query: { "and" : [ "Country is one of (United States of America, Canada, Germany)", { "or" : [ "Geometry POLYGON((-87.86399 41.90845,-87.83014 41.72414,-87.50162 41.68986,-87.6115 42.01609,-87.72057 42.09619,-87.86324 41.99978,-87.86399 41.90845))", "Geometry POLYGON((-79.56024 43.57087,-79.39673 43.58707,-79.26253 43.64758,-79.17464 43.74781,-79.16607 43.77775,-79.36285 43.77901,-79.56487 43.72253,-79.56024 43.57087))", "Geometry POLYGON((7.68529 47.97424,7.68795 47.96576,7.72759 47.91773,7.80457 47.90129,7.87432 47.91031,7.92082 47.96558,7.91131 48.08331,7.82017 48.08851,7.74795 48.06041,7.68529 47.97424))", "Geometry POLYGON((-112.19533 33.71073,-112.25341 33.4781,-112.2114 33.2679,-112.03083 33.18389,-111.80416 33.22571,-111.78702 33.39841,-111.95085 33.70296,-112.19533 33.71073))" ] }, "TaxonKey is one of (Odocoileus virginianus (Zimmermann, 1780), Capreolus capreolus (Linnaeus, 1758), Sciurus vulgaris Linnaeus, 1758, Oryctolagus cuniculus (Linnaeus, 1758), Lepus europaeus Pallas, 1778, Vulpes vulpes (Linnaeus, 1758), Leporidae, Sciurus carolinensis Gmelin, 1788)" ] } The dataset's 18 records were derived from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0190341-230224095556074/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.453 | 0.621 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".