DFO Arctic Region Biodiversity of the Benthic Epifauna Trawl Survey from KEBABB program (2021)
Bibliographic record
Abstract
This resource documents a dataset of epifauna occurrences collected in 2021 during The Knowledge and Ecosystem-Based Approach in Baffin Bay (KEBABB) program developed by the Department of Fisheries and Oceans Canada (DFO) in collaboration with university partners. The overall objective of KEBABB is to characterize the variability and trends in physical, chemical, and biological oceanographic conditions and food webs supporting fisheries in the connected ecosystems of western Baffin Bay and Lancaster Sound. In 2021, DFO expanded the KEBABB program to Barrow Strait (KEBABS-Knowledge and Ecosystem-Based Approach in Barrow Strait), a key productive area of the Tallurutiup Imanga National Marine Conservation Area. The Epibenthic invertebrates were identified from catches collected with an Agassiz trawl and/or a benthic beam trawl. // Cette ressource documente un jeu de données sur les occurrences d'épifaune collectées en 2021 dans le cadre du programme KEBABB (Knowledge and Ecosystem-Based Approach in Baffin Bay) développé par Pêches et Océans Canada (MPO) en collaboration avec des partenaires universitaires. L'objectif général du programme KEBABB est de caractériser la variabilité et les tendances des conditions océanographiques physiques, chimiques et biologiques et des réseaux trophiques soutenant les pêches dans les écosystèmes de l'ouest de la baie de Baffin et du détroit de Lancaster. En 2021, le MPO a étendu le programme KEBABB au détroit de Barrow (KEBABS-Knowledge and Ecosystem-Based Approach in Barrow Strait), une zone productive clé de l'aire marine nationale de conservation de Tallurutiup Imanga. Les invertébrés épibenthiques ont été identifiés à partir des prises collectées par un chalut Agassiz et/ou chalut à perche benthique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".