Bibliographic record
Abstract
A dataset containing 666 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is Specimen", { "or" : [ "Country is Belize", "Country is Guatemala", "Country is Mexico" ] }, { "or" : [ "PublishingOrg is b554c320-0560-11d8-b851-b8a03c50a862", "PublishingOrg is 6ea87510-0561-11d8-b851-b8a03c50a862", "PublishingOrg is ff418020-1d67-11d9-8435-b8a03c50a862", "PublishingOrg is 9c0a8aa8-4ce7-49ba-aac7-21a97234f886" ] }, { "or" : [ "Issue is Coordinate rounded", "Issue is Geodetic datum assumed WGS84", "Issue is Coordinate reprojected" ] }, { "or" : [ "DatasetKey is AMNH Mammal Collections", "DatasetKey is KUBI Mammalogy Collection", "DatasetKey is NMNH Extant Specimen Records (USNM, US)", "DatasetKey is Actualización de la base de datos del estado de Morelos de la Colección Nacional de Mamíferos del Instituto de Biología, UNAM", "DatasetKey is Elaboración del banco de datos de las colecciones del Museo de Zoología-CIQRO", "DatasetKey is Biodiversity Research and Teaching Collections - TCWC Vertebrates", "DatasetKey is University of Michigan Museum of Zoology, Division of Mammals", "DatasetKey is Base de datos de mamíferos de México depositados en colecciones de Estados Unidos y Canadá", "DatasetKey is Mammalogy Collection - Royal Ontario Museum", "DatasetKey is Angelo State Natural History Collections (ASNHC) - Mammalogy Collection" ] }, { "or" : [ "InstitutionCode is ku", "InstitutionCode is ummz", "InstitutionCode is rom", "InstitutionCode is asnhc" ] }, "TaxonKey is Heteromys gaumeri J.A.Allen & Chapman, 1897" ] } The dataset includes 666 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005075-180508205500799/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 | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.487 | 0.590 |
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".