Bibliographic record
Abstract
A dataset containing 777171 species occurrences available in GBIF matching the query: { "and" : [ "Country is Canada", "DatasetKey is iNaturalist Research-grade Observations", "Geometry POLYGON((-81.82892 45.34607,-81.82892 45.10437,-81.53229 44.91211,-81.4444 44.64844,-81.74103 44.32434,-81.91132 43.87939,-81.83441 43.61572,-81.91132 43.37402,-82.16949 43.26965,-82.29034 43.24768,-82.42218 43.24219,-82.61444 42.9071,-83.00995 42.48962,-83.18573 42.24243,-83.2077 42.09961,-83.10883 41.95129,-82.81769 41.88538,-82.46063 41.82495,-82.12555 41.91284,-81.70807 42.23145,-81.08734 42.51709,-80.64789 42.47314,-79.95575 42.45667,-79.80194 42.61047,-79.68109 42.69836,-78.76373 42.8302,-78.75275 43.44543,-78.53302 43.82996,-78.31329 43.86841,-80.16998 45.09338,-80.54352 44.79126,-80.92255 45.02197,-81.10382 45.26367,-81.59821 45.43396,-81.82892 45.34607))", "OccurrenceStatus is Present" ] } The dataset includes 777171 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0085914-210914110416597/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) | 0.261 | 0.419 |
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".