Bibliographic record
Abstract
A dataset containing 6168 species occurrences available in GBIF matching the query: { "and" : [ { "or" : [ "Country is United States of America", "Country is Sweden", "Country is Australia", "Country is France", "Country is Canada", "Country is United Kingdom of Great Britain and Northern Ireland", "Country is Netherlands", "Country is Spain", "Country is Denmark", "Country is Germany", "Country is Thailand", "Country is India", "Country is Philippines", "Country is South Africa", "Country is Lao People’s Democratic Republic", "Country is Indonesia", "Country is Sri Lanka", "Country is Viet Nam", "Country is Malaysia", "Country is Kenya", "Country is Pakistan", "Country is Bangladesh", "Country is Myanmar", "Country is Nepal", "Country is Senegal", "Country is Burundi", "Country is Burkina Faso", "Country is Uganda", "Country is Tanzania, United Republic of", "Country is Guinea", "Country is Côte d’Ivoire", "Country is Papua New Guinea", "Country is Sudan", "Country is Mali", "Country is Cameroon", "Country is Benin", "Country is Nigeria", "Country is Cambodia" ] }, "HasCoordinate is true", "HasGeospatialIssue is false", "OccurrenceStatus is Present", "TaxonKey is Bactrocera dorsalis (Hendel, 1912)" ] } The dataset includes 6168 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0226371-200613084148143/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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.242 | 0.293 |
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".