Bioeconomic analysis accounting for environmental effects in data-poor fisheries: the northern Labrador Arctic char
Bibliographic record
Abstract
Fisheries managers call for more nuanced understandings of complex interactions between exploitation and environmental variability, especially in data poor settings. We develop a bioeconomic model for the Arctic char (Salvelinus alpinus) out of Nain, northern Labrador, incorporating climate variability into growth. We derive parameters necessary for the bioeconomic analysis through optimization and identify optimal equilibrium conditions for the model with and without climate variability. Accounting for variability results in a slightly higher optimal harvest, fishing effort and stock. We find an optimal effort of 591 fishing weeks and harvest of 156 920 kg for 2014, suggesting that both were below optimal. We further find that increased temperature leads to higher optimal effort and net benefits at steady state. Despite numerous uncertainties, data and knowledge gaps limiting the accuracy of our estimates, this is the first effort to identify the equilibrium harvesting conditions for this currently uneconomic, yet socially and culturally important fishery. The methodology can be applicable to other data-deficient fisheries with similar challenges and unknowns, to advance the understanding of socially optimal harvesting and interactions with environmental variability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".