Bibliographic record
Abstract
A dataset listing the 274 species recorded in GBIF matching the query: { "and" : [ { "or" : [ "PublishingCountry is Canada", "Country is Canada" ] }, "Geometry POLYGON((-55.40417 49.21353,-55.44219 49.20011,-55.46666 49.18583,-55.47627 49.16812,-55.48239 49.1424,-55.47277 49.12125,-55.45224 49.08692,-55.4151 49.05629,-55.37345 49.04045,-55.34592 49.03758,-55.31096 49.03529,-55.27295 49.04274,-55.23624 49.05849,-55.20871 49.07023,-55.1825 49.09427,-55.17561 49.12,-55.17823 49.14488,-55.18872 49.17345,-55.21319 49.19344,-55.22717 49.21257,-55.25426 49.22199,-55.28878 49.23283,-55.33134 49.23294,-55.3781 49.22352,-55.40417 49.21353))" ] } The dataset's 274 records were derived from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002796-251120083545085/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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.466 | 0.614 |
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".