Bibliographic record
Abstract
A dataset containing 1634395 species occurrences available in GBIF matching the query: { "and" : [ "Country is Mexico", "HasGeospatialIssue is false", "HasCoordinate is true", "Geometry POLYGON((-126.386718 31.653381,-114.960937 32.249974,-110.742187 29.075375,-107.226562 25.641526,-104.765625 22.593726,-104.238281 19.973348,-102.304687 18.479609,-97.382812 16.636191,-95.273437 16.299051,-91.582031 15.284185,-90.703125 13.923403,-124.980468 11.867350,-126.386718 31.653381))", { "or" : [ "BasisOfRecord is Human Observation", "BasisOfRecord is Machine Observation", "BasisOfRecord is Observation", "BasisOfRecord is Literature Occurrence", "BasisOfRecord is Specimen", "BasisOfRecord is Material sample" ] }, { "or" : [ "PublishingCountry is Germany", "PublishingCountry is Norway", "PublishingCountry is Puerto Rico", "PublishingCountry is Belgium", "PublishingCountry is Finland", "PublishingCountry is Chinese Taipei", "PublishingCountry is Russian Federation", "PublishingCountry is Portugal", "PublishingCountry is Bulgaria", "PublishingCountry is Japan", "PublishingCountry is Denmark", "PublishingCountry is Luxembourg", "PublishingCountry is France", "PublishingCountry is New Zealand", "PublishingCountry is Brazil", "PublishingCountry is Sweden", "PublishingCountry is United Kingdom of Great Britain and Northern Ireland", "PublishingCountry is United States of America", "PublishingCountry is Canada", "PublishingCountry is Estonia", "PublishingCountry is Ghana", "PublishingCountry is Switzerland", "PublishingCountry is South Africa", "PublishingCountry is Korea, Republic of", "PublishingCountry is Italy", "PublishingCountry is Colombia", "PublishingCountry is Spain", "PublishingCountry is Costa Rica", "PublishingCountry is Venezuela (Bolivarian Republic of)", "PublishingCountry is Argentina", "PublishingCountry is Austria", "PublishingCountry is Australia", "PublishingCountry is Czechia", "PublishingCountry is Nicaragua", "PublishingCountry is Poland", "PublishingCountry is Netherlands" ] } ] } The dataset includes 1634395 records from 399 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084951-160910150852091/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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.230 | 0.332 |
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".