Bibliographic record
Abstract
A dataset containing 1016 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is Human Observation", "Country is Canada", "Year 1975-2019", "Geometry POLYGON((-95.28442 49.02562,-94.63074 48.75728,-93.35358 48.6516,-91.45569 48.10853,-89.53308 48.01308,-88.77502 47.41434,-88.36853 47.05628,-84.96277 46.83126,-84.96277 46.52977,-84.24866 46.39355,-83.83118 45.91409,-84.51782 45.86171,-83.26538 45.43161,-83.11157 44.35763,-83.66089 43.72586,-83.26538 44.02679,-82.76001 44.15305,-82.38647 43.08734,-83.04565 42.23096,-83.04565 41.82291,-78.91479 42.87838,-79.0686 43.43936,-76.76147 43.8052,-76.05835 44.35763,-75.20142 44.93603,-74.58618 45.09136,-71.53198 45.09136,-71.3562 45.35446,-70.58716 45.73916,-70.14771 46.59133,-69.42261 47.41545,-69.00513 47.56392,-69.09302 47.23673,-68.28003 47.41545,-67.84058 47.07237,-67.77466 45.75449,-66.58813 44.82705,-66.31348 42.75669,-59.10645 45.46938,-58.84277 47.10902,-53.74512 45.89918,-51.06445 47.41619,-54.75586 55.29413,-63.54492 61.70237,-59.85352 67.0777,-75.49805 73.63522,-77.25586 75.80858,-75.32227 77.62336,-68.4668 80.24297,-63.19336 81.36525,-59.94141 82.59291,-58.53516 83.48635,-96.15234 83.44635,-95.28442 49.02562))", "TaxonKey is Vulpes vulpes (Linnaeus, 1758)", "HasGeospatialIssue is false" ] } The dataset includes 1016 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022293-200613084148143/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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.237 | 0.359 |
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".