Bibliographic record
Abstract
A dataset containing 55386 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is Human Observation", "DatasetKey is one of (Maritimes Summer Research Vessel Surveys, Maritimes Spring Research Vessel Surveys, DFO Quebec Region Biodiversity of the Planning for Integrated Environmental Response Coastal Survey in the St. Lawrence Estuary and Gulf (2017-2021), Northeast Area Monitoring and Assessment Program Near Shore Trawl Survey (NEAMAP), Maritimes 4VSW Research Vessel Surveys, Programme CROMIS: carnet de plongée en ligne de la FFESSM-Observations d'espèces subaquatiques collectées par les utilisateurs de CROMIS, Programme BioObs: observations naturalistes en milieux aquatiques-Observations de BioObs., iNaturalist Research-grade Observations, Maritimes Fall Research Vessel Surveys, Northeast Fisheries Science Center Bottom Trawl Survey Data, ECNASAP - East Coast North America Strategic Assessment, Maine Department of Marine Resources Inshore Trawl Survey 2000-2019, DFO Quebec Region Magdalen Islands Lobster Survey, DFO Quebec Region Magdalen Islands Sea Scallop Survey 1992-2019, Diveboard - Scuba diving citizen science observations, Artportalen, Verified marine records from Indicia-based surveys, DFO Quebec Region Magdalen Islands Sea Scallop Survey 2021-2022, National Invasive Species Database, NaGISA Project, DFO Quebec Region Ecosystemic bottom trawl surveys 2004-2022)", "TaxonKey is Homarus americanus H.Milne Edwards, 1837" ] } The dataset includes 55386 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022888-241107131044228/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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.286 | 0.345 |
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".