Bibliographic record
Abstract
A dataset containing 25222 species occurrences available in GBIF matching the query: { "TaxonKey" : [ "is one of (Quercus salicifolia Née, Quercus acutifolia Née, Quercus aerea Trel., Quercus ajoensis C.H.Mull., Quercus alpescens Trel., Quercus rugosa Née, Quercus barrancana Spellenb., Quercus benthamii A.DC., Quercus brandegeei Goldman, Quercus breedloveana Nixon & Barrie, Quercus carmenensis C.H.Mull., Quercus cedrosensis C.H.Mull., Quercus coahuilensis Nixon & C.H.Müll., Quercus coffeicolor Trel., Quercus cortesii Liebm., Quercus costaricensis Liebm., Quercus crispifolia Trel., Quercus crispipilis Trel., Quercus cualensis L.M.González, Quercus xalapensis Bonpl., Quercus delgadoana S.Valencia, Nixon & L.M.Kelly, Quercus deliquescens C.H.Mull., Quercus devia Goldman, Quercus diversifolia Née, Quercus dumosa Nutt., Quercus palmeri (Engelm.) Engelm., Quercus durifolia Seemen ex Loes., Quercus edwardsiae C.H.Mull., Quercus engelmannii Greene, Quercus seemannii Liebm., Quercus flocculenta C.H.Mull., Quercus furfuracea Liebm., Quercus galeanensis C.H.Mull., Quercus ghiesbreghtii M.Martens & Galeotti, Quercus canbyi Trel., Quercus gracilior C.H.Mull., Quercus grahamii Benth., Quercus gulielmitreleasei C.H.Mull., Quercus hinckleyi C.H.Mull., Quercus hintonii E.F.Warb., Quercus hintoniorum Nixon & C.H.Müll., Quercus hirtifolia M.L.Vázquez, S.Valencia & Nixon, Quercus ignaciensis C.H.Mull., Quercus iltisii L.M.González, Quercus insignis M.Martens & Galeotti, Quercus laxa Liebm., Quercus macdougallii Martínez, Quercus mcvaughii Spellenb., Quercus meavei Valencia-A, Sabas & O.J.Soto, Quercus melissae Nixon & Barrie, Quercus miquihuanensis Nixon & C.H.Müll., Quercus mulleri Martínez, Quercus nixoniana S.Valencia & Lozada-Pérez, Quercus oleoides Schltdl. & Cham., Quercus oocarpa Liebm., Quercus opaca Trel., Quercus peninsularis Trel., Quercus perpallida Trel., Quercus pinnativenulosa C.H.Mull., Quercus laceyi Small, Quercus purulhana Trel., Quercus radiata Trel., Quercus rekonis Trel., Quercus rysophylla Weath., Quercus robusta C.H.Mull., Quercus runcinatifolia Trel. & C.H.Müll., Quercus saltillensis Trel., Quercus sarahmariae Nixon & Barrie, Quercus skinneri Benth., Quercus supranitida C.H.Mull., Quercus tardifolia C.H.Mull., Quercus tinkhamii C.H.Mull., Quercus tomentella Engelm., Quercus toumeyi Sarg., Quercus leiophylla A.DC., Quercus ocoteifolia Liebm., Quercus tuitensis L.M.González, Quercus undata Trel., Quercus verde C.H.Mull., Quercus vicentensis Trel.)" ] } The dataset includes 25222 records from 264 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0272762-200613084148143/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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.217 | 0.346 |
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".