Acoustic Telemetry Reveals the Complex Nature of Mixed-Stock Fishing in Canada's Largest Arctic Char Commercial Fishery
Bibliographic record
Abstract
Abstract Climate change is having a myriad of effects on Arctic ecosystems, yet understanding how these changes will influence the spatiotemporal dynamics of harvest in northern commercial fisheries remains unclear. Furthermore, stock mixing continues to complicate fisheries management in Arctic Canada, especially for anadromous stocks, but data on the extent and degree of stock mixing for the majority of northern fisheries are scarce. Here, we used a multiyear (2015–2019) acoustic telemetry data set to test the utility of acoustic telemetry as a potential tool for inferring stock mixing in the Arctic Char Salvelinus alpinus commercial fishery in Cambridge Bay (Nunavut). We also assessed the effect of annual variation in environmental variables (river breakup and marine ice conditions) on the potential contribution of discrete stocks to commercial harvest at several fisheries. We found that stock mixing during the commercial harvest is common in both marine and freshwater fisheries during the summer/open-water season, with virtually all stocks potentially being susceptible to harvest at any given commercial fishery. Additionally, in some fisheries, the vulnerability of different stocks to harvest was influenced by annual differences in marine ice and river breakup conditions. We discuss options for fisheries management, including a potential quota-transfer system, and highlight how changing environmental and climatic conditions may have an effect on the commercial harvest of Arctic Char in the region. Overall, the results of this study demonstrate the utility of acoustic telemetry for informing mixed-stock fisheries while highlighting the complex and pervasive nature of stock mixing in Canada's largest Arctic Char commercial fishery.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".