Bibliographic record
Abstract
A dataset containing 297 species occurrences available in GBIF matching the query: { "and" : [ "DatasetKey is one of (Naturalis Biodiversity Center (NL) - Paleontology, Sharing vertebrate occurrence data from camera traps in Asia, iNaturalist Research-grade Observations, Animal Sound Archive, WCS Cambodia camera trap occurrence data, NMNH Extant Specimen Records (USNM, US), Natural History Museum (London) Collection Specimens, RBINS Mammal collection, The mammals collection (ZM) of the Muséum national d'Histoire naturelle (MNHN - Paris), RBINS DaRWIN, Mammals housed at MHNG, Geneva, Paleobiology Database, NMNH Material Samples (USNM), Field Museum of Natural History (Zoology) Mammal Collection, Museum of Comparative Zoology, Harvard University, naturgucker, MSB Mammal Collection (Arctos), Mammalogy Collection - Royal Ontario Museum, Ghent University - Zoology Museum - Vertebrate collection, SUI Vertebrate Collection, MVZ Mammal Collection (Arctos), Brigham Young University Life Science Museum (BYU) Mammal Collection (Arctos), Angelo State Natural History Collections (ASNHC) Mammal specimens (Arctos), Rapid Assessment Program (RAP) Biodiversity Survey Database, Mammals of the Natural Laboratory of Peat-Swamp Forest, Sebangau National Park, Central Kalimantan, Indonesia, Mammalia (Luomus), UTEP Mammals (Arctos))", "HasCoordinate is true", "HasGeospatialIssue is false", "TaxonKey is Helarctos malayanus (Raffles, 1822)" ] } The dataset includes 297 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082685-240229165702484/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 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.411 | 0.423 |
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".