Bibliographic record
Abstract
A dataset containing 140 species occurrences available in GBIF matching the query: { "and" : [ "Country is United States of America", "DatasetKey is one of (University of Michigan Herbarium, A global database for the distributions of crop wild relatives, Royal Ontario Museum Green Plant Herbarium (TRT), Central Michigan University Herbarium, G. S. Torrey Herbarium at the University of Connecticut (CONN), Morton Arboretum - Vascular Plants, Field Museum of Natural History (Botany) Seed Plant Collection, CHAS Botany Collection (Arctos), Michigan State University Herbarium Vascular Plants)", "Geometry POLYGON((-87.31435 46.51278,-87.46441 41.49606,-81.94034 41.45555,-81.91055 43.48909,-83.58995 46.23723,-84.94429 46.87913,-87.31435 46.51278))", "HasGeospatialIssue is false", "TaxonKey is Malus coronaria (L.) Mill." ] } The dataset includes 140 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071489-241126133413365/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 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.140 | 0.209 |
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".