Bibliographic record
Abstract
A dataset containing 1846 species occurrences available in GBIF matching the query: { "and" : [ "Continent is Asia", "Country is one of (Chinese Taipei, Hong Kong, China, Macao)", "DatasetKey is one of (iNaturalist Research-grade Observations, The Taiwan Roadkill Observation Network Data Set., Investigation Current Status of River in Taiwan between 2002 to 2022, Museum of Comparative Zoology, Harvard University, MVZ Herp Collection (Arctos), International Barcode of Life project (iBOL), Taxonomic revision of the king cobra Ophiophagus hannah (Cantor, 1836) species complex (Reptilia: Serpentes: Elapidae), with the description of two new species, Royal Ontario Museum - Herpetology Collection, INSDC Sequences, Brigham Young University (BYU) Herpetology Collection (Arctos))", "HasCoordinate is true", "HasGeospatialIssue is false", "OccurrenceStatus is Present", "TaxonKey is one of (Naja atra, Naja kaouthia, Bungarus multicinctus wanghaotingi, Bungarus suzhenae, Bungarus bungaroides, Bungarus fasciatus, Bungarus lividus, Bungarus niger, Ophiophagus hannah, Sinomicrurus kelloggi, Sinomicrurus swinhoei, Sinomicrurus macclellandi swinhoei, Sinomicrurus macclellandi macclellandi)" ] } The dataset includes 1846 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010594-250827131500795/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 | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.365 | 0.443 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".