Bibliographic record
Abstract
A dataset containing 713 species occurrences available in GBIF matching the query: { "and" : [ "Country is Canada", "DatasetKey is iNaturalist Research-grade Observations", "Geometry POLYGON((-81.80969 45.26367,-81.41418 44.69238,-81.71082 44.35181,-81.84265 43.64868,-82.13928 43.26416,-82.40295 43.02246,-82.45789 42.81372,-82.50183 42.64893,-82.66663 42.51709,-82.95227 42.35229,-83.09509 42.22046,-83.09509 42.04468,-82.91931 41.90186,-82.53479 41.79199,-81.9635 42.08862,-81.50208 42.38525,-80.80994 42.49512,-80.34851 42.44019,-79.953 42.55005,-80.17273 42.72583,-79.54651 42.7478,-79.00818 42.81372,-78.9093 42.90161,-79.03015 43.2312,-79.3158 43.2312,-79.57947 43.34106,-79.09607 43.64868,-78.63464 43.75854,-78.5907 44.02222,-79.19495 44.23096,-79.78821 44.50562,-81.00769 45.40649,-81.80969 45.26367))", "TaxonKey is Lithobates sylvaticus (LeConte, 1825)" ] } The dataset includes 713 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064800-210914110416597/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 | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.292 | 0.458 |
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".