Bibliographic record
Abstract
A dataset containing 1091 species occurrences available in GBIF matching the query: { "and" : [ "Country is United States of America", { "or" : [ "DatasetKey is University of Michigan Museum of Zoology, Division of Mollusks", "DatasetKey is NMNH Extant Specimen Records (USNM, US)", "DatasetKey is Canadian Museum of Nature Mollusc Collection", "DatasetKey is Museum of Comparative Zoology, Harvard University", "DatasetKey is NCSM Mollusk Collection", "DatasetKey is Delaware Museum of Nature and Science – Mollusks", "DatasetKey is Field Museum of Natural History (Zoology) Invertebrate Collection", "DatasetKey is Auburn University Museum of Natural History Mollusks", "DatasetKey is Malacology Collection at the Academy of Natural Sciences of Philadelphia" ] }, "HasCoordinate is true", "HasGeospatialIssue is false", "TaxonKey is Ligumia recta (Lamarck, 1819)" ] } The dataset includes 1091 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021947-210914110416597/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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| 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) | 0.251 | 0.326 |
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".