Bibliographic record
Abstract
A dataset containing 59976 species occurrences available in GBIF matching the query: { "and" : [ "HasCoordinate is TRUE", "HasGeospatialIssue is FALSE", "Geometry POLYGON((-100.551 36.917,-71.79 36.917,-71.79 49.612,-100.551 49.612,-100.551 36.917))", "Year is greater than or equal to 1970", "RecordedBy is one of (Glenn Barrett, Glenn Perricone, Gone But Not Forgotten - R.I.P, Graeme Laubach, Gray Catanzaro, Greg Coniglio, Greg Lawrence, Greg Meredith, Guelph Lake Nature Centre, Guy Mayer, Gwendolyn Temple, Hamilton Turner, Hannah McCurdy-Adams, Hannah Mews, Heather, Helena, Henrique Pacheco, Herpihiko Komatsu, Hunter Meinke, Huy Chi Truong, IHUNTA, Ian Johnson, Inland Seas Education Association, Isaac, J. Burke Korol, Jack Farley, Jackson MacNeal, Jackson Nesbit, Jacob Bowman, Jacob Carroll, Jacob Collison, Jacob E. Norton, Jacob Saucier, Jacob Schick, Jake, Jake OFlaherty, Jakob Mueller, James, James Holdsworth, James Maughn, James Parham, James Yates, Janae Taylor, Janet, Janet Gingold, Jared Gorrell, Jason L Miller, Jason McNees, Jazagiabern, Jazz (aka turtlehelper))", "TaxonKey is Chordata" ] } The dataset includes 59976 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002161-250221090833381/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 | 0.007 | 0.011 |
| 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) | 0.291 | 0.418 |
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".