Fish, Invertebrate, and Marine Mammal Bycatch in a Central Canadian Commercial Fishery for Arctic Char
Bibliographic record
Abstract
Abstract Cambridge Bay, Nunavut, hosts Canada’s largest Arctic Char Salvelinus alpinus commercial fishery with waterbody-specific quotas managed under one Integrated Fisheries Management Plan that emphasizes ecosystem-based management and the need to understand bycatch in the fishery. Bycatch reporting in the fishery, however, remains deficient. In this study, we report on fish, invertebrate, and marine mammal bycatch recorded in logbooks from 2012-2018 at two commercial waterbodies fished using weirs (Halokvik, Jayko) and two fished using gillnets (Surrey, Ekalluk) in multiple Arctic char habitats (rivers, lakes, estuaries). Arctic Char not retained for commercial purposes (discards or those kept for subsistence) comprised the greatest amount of bycatch. Other bycatch included seven fish species(n = 633), one crab species (n = 5), and two seal species (n = 11). Weir fisheries had minimal non-target bycatch, with Halokvik reporting none and Jayko only one species. Gillnet fisheries exhibited the highest bycatch diversity, particularly at the Surrey estuarine fishery. Significant inter-annual variation in the amount of non-target bycatch was also observed at three of the four waterbodies. These findings offer insights for ecosystem-based management for this fishery, while providing a baseline for future monitoring.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".