Bibliographic record
Abstract
A dataset listing the 655 species recorded in GBIF matching the query: { "and" : [ { "or" : [ "Geometry POLYGON((-91.383 46.687,-91.311 46.687,-91.311 46.791,-91.383 46.791,-91.383 46.687))", "Geometry POLYGON((-91.24 46.765,-91.196 46.765,-91.196 46.851,-91.24 46.851,-91.24 46.765))", "Geometry POLYGON((-91.151 46.801,-91.035 46.801,-91.035 46.856,-91.151 46.856,-91.151 46.801))", "Geometry POLYGON((-90.886 46.692,-90.856 46.692,-90.856 46.703,-90.886 46.703,-90.886 46.692))", "Geometry POLYGON((-90.947 46.669,-90.888 46.669,-90.888 46.809,-90.947 46.809,-90.947 46.669))", "Geometry POLYGON((-91.027 46.809,-90.824 46.809,-90.824 46.906,-91.027 46.906,-91.027 46.809))" ] }, { "or" : [ "DatasetKey is Marsh Monitoring Program - Amphibians", "DatasetKey is USGS Nonindigenous Aquatic Species database", "DatasetKey is Illinois Natural History Survey Insect Collection", "DatasetKey is Michigan State University Herbarium Lichens", "DatasetKey is Canadian Museum of Nature Herbarium", "DatasetKey is NMNH Extant Specimen Records (USNM, US)", "DatasetKey is Lund University Biological Museum - Botanical collection (LD)", "DatasetKey is Canadian Museum of Nature Mollusc Collection", "DatasetKey is Marsh Monitoring Program - Birds", "DatasetKey is INHS wet collections accession", "DatasetKey is University of Michigan Herbarium" ] } ] } The dataset's 655 records were derived from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008599-190320150433242/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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.481 | 0.633 |
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".