Bibliographic record
Abstract
A dataset containing 59 species occurrences available in GBIF matching the query: { "and" : [ { "or" : [ "BasisOfRecord is Literature Occurrence", "BasisOfRecord is Specimen", "BasisOfRecord is Living Specimen" ] }, "Country is Mexico", "Year 1912-2019", { "or" : [ "DatasetKey is MSU Mammalogy, Ornithology and Vertebrate Paleontology Collections", "DatasetKey is Field Museum of Natural History (Zoology) Mammal Collection", "DatasetKey is Sistematización de las colecciones científicas del Instituto de Historia Natural y Ecología, (IHNE) Chiapas", "DatasetKey is NMNH Extant Specimen Records (USNM, US)", "DatasetKey is KUBI Mammalogy Collection", "DatasetKey is Biodiversity Research and Teaching Collections - TCWC Vertebrates", "DatasetKey is Museum of Comparative Zoology, Harvard University", "DatasetKey is LSUMZ Mammals Collection", "DatasetKey is Mammalogy Collection - Royal Ontario Museum", "DatasetKey is Fortalecimiento de las colecciones de ECOSUR. Primera fase (Mamíferos San cristóbal)" ] }, "HasCoordinate is true", "TaxonKey is Leopardus pardalis (Linnaeus, 1758)", "HasGeospatialIssue is false" ] } The dataset includes 59 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003509-191105090559680/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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.411 | 0.509 |
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".