Bibliographic record
Abstract
A dataset containing 29633 species occurrences available in GBIF matching the query: { "and" : [ { "or" : [ "Country is Canada", "Country is United States of America" ] }, "Geometry POLYGON((-89.58004 28.79825,-77.70289 23.07531,-54.20734 46.05333,-55.65641 49.98651,-59.38257 54.02319,-66.65377 56.01996,-67.43006 55.50244,-70.12118 55.70945,-82.95575 54.6744,-88.38974 53.22534,-91.80539 51.15525,-94.44476 48.98165,-94.13424 47.37733,-93.56497 46.8598,-94.2895 46.34228,-93.92723 45.566,-92.94394 44.84146,-92.0124 44.4792,-91.23611 43.80642,-90.8221 42.66787,-90.30457 41.83983,-90.9256 41.27056,-91.44312 40.54602,-91.02911 39.56273,-90.35633 38.9417,-90.04581 38.16542,-89.47654 37.7514,-89.16602 36.81986,-89.8388 35.31904,-91.28787 33.45596,-91.28787 32.26565,-91.49488 31.1271,-90.51158 30.04031,-89.58004 28.79825))", "OccurrenceStatus is Present", "TaxonKey is Lithobates catesbeianus (Shaw, 1802)" ] } The dataset includes 29633 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0391017-210914110416597/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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.246 | 0.360 |
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".