Bibliographic record
Abstract
A dataset containing 40 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is Human Observation", "CoordinateUncertaintyInMeters 0.0-14499.0", "DatasetKey is one of (Galiano Island BC Canada Marine Zoology 1893–2021, Happywhale - Killer Whale in North Pacific Ocean, Base de données Pecheker (modifiée). Museum National D'Histoire Naturelle - DEPARTEMENT MILIEUX PEUPLEMENTS AQUATIQUES, Observation.org, Nature data from around the World, Norwegian Species Observation Service, Happywhale - Killer Whale in Southern Ocean, Land-based observations of cetaceans in the waters of Beringa Island in 2007-2009, Inventaire des Mammifères de France - Inventaire des Mammifères marins de France (métropole et outre-mer), DOF/BirdLife Denmark - Observations from DOFbasen, Artportalen (Swedish Species Observation System), Jordal)", "Geometry POLYGON((-32.21869 12.28623,-5.81635 12.28623,-5.81635 44.96239,-32.21869 44.96239,-32.21869 12.28623))", "HasCoordinate is true", "HasGeospatialIssue is false", "OccurrenceStatus is Present", "TaxonKey is one of (Balaenoptera physalus (Linnaeus, 1758), Balaenoptera musculus (Linnaeus, 1758))", "Year 2005-2022" ] } The dataset includes 40 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0172308-220831081235567/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 | 0.009 |
| 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) | 0.322 | 0.406 |
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".