Bibliographic record
Abstract
A dataset containing 74535661 species occurrences available in GBIF matching the query: { "and" : [ { "or" : [ "Continent is Asia", "Continent is Africa", "Continent is Europe", "Continent is North America", "Continent is South America" ] }, { "or" : [ "Country is United States of America", "Country is Australia", "Country is Sweden", "Country is Canada", "Country is France", "Country is United Kingdom of Great Britain and Northern Ireland", "Country is Netherlands", "Country is Spain", "Country is Germany", "Country is Norway" ] }, { "or" : [ "DatasetKey is EOD – eBird Observation Dataset", "DatasetKey is Artportalen (Swedish Species Observation System)", "DatasetKey is Observation.org, Nature data from around the World", "DatasetKey is DOF/BirdLife Denmark - Observations from DOFbasen", "DatasetKey is Norwegian Species Observation Service", "DatasetKey is INPN - Données flore des CBN agrégées par la FCBN", "DatasetKey is iNaturalist Research-grade Observations", "DatasetKey is Southern African Bird Atlas Project 2", "DatasetKey is Waarnemingen.be - Bird occurrences in Flanders and the Brussels Capital Region, Belgium", "DatasetKey is NSW BioNet Atlas" ] }, "HasCoordinate is true", { "or" : [ "Month is January", "Month is February", "Month is March", "Month is April", "Month is May", "Month is June", "Month is July" ] } ] } The dataset includes 74535661 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056079-200613084148143/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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| 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) | 0.248 | 0.357 |
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".