Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A dataset containing 586755 species occurrences available in GBIF matching the query: { "and" : [ "TaxonKey is one of (Anax junius (Drury, 1773), Apis mellifera Linnaeus, 1758, Aquilegia coerulea James, Argia vivida Hagen, 1865, Ariolimax columbianus (A.Gould, 1851), Boletus edulis Bull., Bouteloua gracilis (Kunth) Lag. ex Griffiths, Calochortus nuttallii Torr., Cantharellus californicus D.Arora & Dunham, Cantharellus formosus Corner, Carnegiea gigantea (Engelm.) Britton & Rose, Castilleja linariifolia Benth., Echinocereus triglochidiatus Engelm., Eriocoma hymenoides (Roem. & Schult.) Rydb., Eschscholzia californica Cham., Lewisia rediviva Pursh, Libellula quadrimaculata Linnaeus, 1758, Mahonia aquifolium (Pursh) Nutt., Myosotis alpestris F.W.Schmidt, Nassella pulchra (Hitchc.) Barkworth, Papilio multicaudata Kirby, 1884, Elymus smithii (Rydb.) Gould, Pepsis thisbe Lucas, 1895, Philadelphus lewisii Pursh, Pseudoroegneria spicata (Pursh) Á.Löve, Yucca glauca Nutt.)", "Country is one of (United States of America, Canada, Mexico)", "HasCoordinate is true", "HasGeospatialIssue is false" ] } The dataset includes 586755 records from 548 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003355-250310093411724/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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.143 |
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 it