Bibliographic record
Abstract
A dataset containing 626 species occurrences available in GBIF matching the query: { "and" : [ { "or" : [ "BasisOfRecord is Specimen", "BasisOfRecord is Literature Occurrence" ] }, { "or" : [ "Country is Guatemala", "Country is Costa Rica", "Country is Panama", "Country is Mexico", "Country is El Salvador", "Country is Belize", "Country is Honduras", "Country is Nicaragua", "Country is Colombia" ] }, { "or" : [ "PublishingOrg is 8edbbde0-055e-11d8-b850-b8a03c50a862", "PublishingOrg is 6ea87510-0561-11d8-b851-b8a03c50a862", "PublishingOrg is b554c320-0560-11d8-b851-b8a03c50a862", "PublishingOrg is ff418020-1d67-11d9-8435-b8a03c50a862", "PublishingOrg is bc092ff0-02e4-11dc-991f-b8a03c50a862", "PublishingOrg is 7b8aff00-a9f8-11d8-944b-b8a03c50a862" ] }, { "or" : [ "Issue is Geodetic datum assumed WGS84", "Issue is Coordinate reprojected", "Issue is Coordinate rounded" ] }, { "or" : [ "DatasetKey is NMNH Extant Specimen Records (USNM, US)", "DatasetKey is University of Michigan Museum of Zoology, Division of Mammals", "DatasetKey is Mammalogy Collection - Royal Ontario Museum", "DatasetKey is MVZ Mammal Collection (Arctos)", "DatasetKey is KUBI Mammalogy Collection", "DatasetKey is Field Museum of Natural History (Zoology) Mammal Collection", "DatasetKey is AMNH Mammal Collections", "DatasetKey is USAC Mammals Collection" ] }, "TaxonKey is Heteromys desmarestianus Gray, 1868" ] } The dataset includes 626 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014706-190621201848488/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) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| 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) | 0.399 | 0.483 |
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".