Bibliographic record
Abstract
A dataset containing 7593 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is one of (Machine Observation, Material sample, Specimen, Fossil, Material citation, Occurrence evidence)", "Continent is one of (North America, South America)", "Country is one of (United States of America, Mexico, Colombia, Venezuela (Bolivarian Republic of), El Salvador, Nicaragua, Costa Rica, Guatemala, Canada, Honduras)", "HasCoordinate is true", "HasGeospatialIssue is false", "Issue is one of (Geodetic datum assumed WGS84, Coordinate reprojected, Continent derived from coordinates, Country derived from coordinates, Taxon match higher rank, The taxon ID was not used in backbone matching, The scientific name ID was not used in backbone matching, Elevation min max swapped, Occurrence status inferred from individual count, Ambiguous institution, Institution match none, Institution match fuzzy, Different owner institution, Collection match fuzzy)", "OccurrenceStatus is Present", "TaxonKey is Sylvilagus floridanus (J.A.Allen, 1890)", "Year 1901-2023" ] } The dataset includes 7593 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001000-230918134249559/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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.209 | 0.338 |
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".