Small‐scale Arctic charr <i>Salvelinus alpinus</i> fisheries in Canada's Nunavut: management challenges and options
Bibliographic record
Abstract
The Arctic charr Salvelinus alpinus is a diverse and abundant resource in Canada's Nunavut. The anadromous form is primarily targeted by exploitation in small-scale fisheries. The continued importance of subsistence fisheries and growing interest in further developing commercial fisheries underline the need for proper management of S. alpinus in northern Canada. This paper presents the current state of S. alpinus fisheries in Nunavut and related management challenges. An alternate framework for assessment using life-history information as it determines stock productivity and resilience to harvesting is presented. This framework combines (1) a risk assessment tool [productivity-susceptibility analysis (PSA)] to evaluate the relative vulnerability of S. alpinus stocks to harvest and (2) a conceptual model for quantitative assessment to determine sustainable harvest levels. Diversity in S. alpinus life history and contrast in vulnerability scores derived from PSA assessment are demonstrated for a sample of 86 anadromous stocks from throughout Nunavut. These data provide evidence in support of an alternate strategy for assessment permitting to integrate diversity in S. alpinus life history for improved generalization and representativeness. Salvelinus alpinus fisheries in Arctic regions exemplify the need for stock assessment and management alternatives to ensure fish conservation in remote, sensitive ecosystems and in data-poor circumstances.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".