Bibliographic record
Abstract
A dataset containing 4959 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is Specimen", { "or" : [ "Country is France", "Country is Spain", "Country is Germany", "Country is Sweden", "Country is United Kingdom of Great Britain and Northern Ireland", "Country is Portugal", "Country is United States of America", "Country is Belgium", "Country is Israel", "Country is Italy", "Country is Greece", "Country is Morocco", "Country is Canada", "Country is Mexico", "Country is Algeria", "Country is Tunisia", "Country is Türkiye", "Country is Lebanon", "Country is Syrian Arab Republic", "Country is Bulgaria" ] }, { "or" : [ "PublishingOrg is 0363cbd4-f666-455e-8e86-0bbddcf51950", "PublishingOrg is 19456090-b49a-11d8-abeb-b8a03c50a862", "PublishingOrg is 7b8aff00-a9f8-11d8-944b-b8a03c50a862", "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d", "PublishingOrg is 6c4a0bb0-2a4d-11d8-aa2d-b8a03c50a862", "PublishingOrg is 3124bdd8-b38c-46f0-b354-5e1d2c2cd9d2", "PublishingOrg is ae447c50-b8a8-11d8-92a4-b8a03c50a862", "PublishingOrg is 2cd829bb-b713-433d-99cf-64bef11e5b3e", "PublishingOrg is 8483a1f0-1032-11db-ae00-b8a03c50a862", "PublishingOrg is 253fc67c-c7fb-4c85-88f5-43ac6d9dc8a9" ] }, { "or" : [ "Issue is Geodetic datum assumed WGS84", "Issue is Coordinate rounded" ] }, "MediaType is Image", "Year 1000-2018", "HasCoordinate is true", "TaxonKey is Cistaceae", "HasGeospatialIssue is false" ] } The dataset includes 4959 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018089-181108115102211/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 | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.317 | 0.390 |
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".