Bibliographic record
Abstract
A dataset containing 2200 species occurrences available in GBIF matching the query: { "and" : [ "Continent is North America", "Country is Mexico", "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations, Atlas de las Aves de México: Fase II, AMNH Bird Collection, Delaware Museum of Nature & Science — Birds, MLZ Bird Collection (Arctos), CNAV/Coleccion Nacional de Aves, MVZ Bird Collection (Arctos), Ornithology Collection Non Passeriformes - Royal Ontario Museum, CAS Ornithology (ORN))", "Geometry POLYGON((-104.59725 22.42449,-104.30149 22.42449,-104.18575 22.68167,-104.08288 22.81027,-104.13432 23.09317,-103.7614 23.6847,-103.86427 24.0319,-103.65852 24.10905,-103.36276 24.39196,-102.79695 24.32766,-102.51405 24.41768,-102.64264 25.02206,-102.88697 24.76488,-103.2856 25.0092,-103.31132 25.40784,-103.45918 25.44333,-103.3499 25.7679,-103.31132 26.20512,-103.36276 26.59089,-104.36578 26.78378,-104.66155 26.39801,-106.06321 26.75806,-106.6033 25.57501,-107.01479 25.66503,-107.1691 25.56215,-107.15625 25.11208,-106.56472 24.32766,-106.15322 24.32766,-105.99891 23.92902,-105.74172 23.32464,-105.11162 22.926,-104.59725 22.42449))", "HasCoordinate is true", "HasGeospatialIssue is false", "OccurrenceStatus is Present", "TaxonKey is Melanerpes aurifrons (Wagler, 1829)", "Year 1000-2023" ] } The dataset includes 2200 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039920-230224095556074/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 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.285 | 0.426 |
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".