Bibliographic record
Abstract
A dataset containing 11138 species occurrences available in GBIF matching the query: { "and" : [ "Country is Canada", { "or" : [ "DatasetKey is Canadian Museum of Nature Mollusc Collection", "DatasetKey is Maritimes Summer Research Vessel Surveys", "DatasetKey is British Columbia Geoduck (Panopea generosa) Age, Size Structure and Growth Parameters, 1993 - 2002", "DatasetKey is Index Site Surveys Data for Olympia Oysters, Ostrea lurida, in British Columbia – 2009 to 2017", "DatasetKey is Maritimes Spring Research Vessel Surveys", "DatasetKey is iNaturalist Research-grade Observations", "DatasetKey is University of Michigan Museum of Zoology, Division of Mollusks", "DatasetKey is Royal BC Museum - Invertebrates Collection", "DatasetKey is Paleobiology Database", "DatasetKey is Invertebrate Paleontology Division, Yale Peabody Museum" ] }, "TaxonKey is Unionida" ] } The dataset includes 11138 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043729-210914110416597/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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 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) | 0.206 | 0.279 |
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".