Bibliographic record
Abstract
A dataset containing 69499 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is Human Observation", "Country is Canada", "Geometry POLYGON ((-82.9248046875 41.83682786072714, -79.1015625 42.90816007196054, -79.0576171875 43.45291889355465, -76.46484375 43.929549935614595, -75.05859375 44.933696389694674, -80.9033203125 46.01222384063236, -79.5849609375 44.809121700077355, -80.1123046875 44.653024159812, -82.5732421875 46.01222384063236, -81.5185546875 44.33956524809713, -81.8701171875 43.89789239125797, -81.82617187499999 43.42100882994726, -82.30957031249999 43.004647127794435, -82.5732421875 42.52069952914966, -83.1884765625 42.32606244456202, -82.9248046875 41.83682786072714))", { "or" : [ "DatasetKey is iNaturalist Research-grade Observations", "DatasetKey is USGS Great Lakes Science Center Research Vessel Catch Information System Trawl", "DatasetKey is Ontario BioBlitz Species Records", "DatasetKey is USGS Nonindigenous Aquatic Species database", "DatasetKey is naturgucker", "DatasetKey is Earth Guardians Weekly Feed", "DatasetKey is Inventory and BioBlitz Records from rare Charitable Research Reserve" ] }, "HasCoordinate is true", "HasGeospatialIssue is false" ] } The dataset includes 69499 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002859-171124123535762/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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.288 | 0.431 |
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".