Navigating Nunatsiavut’s Arctic Charr: A Simultaneous Commercial and Subsistence Fishery with Many Unknowns
Bibliographic record
Abstract
We explore optimal harvest conditions for Nunatsiavut’s Arctic charr, a data-deficient yet economically and culturally important fishery for Labrador Inuit. In the past, arbitrarily set quotas in the absence of data on science and climate shifts have led to sustainability concerns. The fishery, adhering to conservation principles, continues at low intensity today, so as to support local employment and maintain sociocultural values, despite its low economic viability. Using the only available data for Nain’s commercial fishery, we estimate intrinsic growth, catchability, and carrying capacity, which we then use in a bioeconomic model to estimate maximum economic yield. Results indicate that foregone commercial harvests are 75%–93% below optimal, before accounting for subsistence harvest. Improved understanding of the conditions under which subsistence and commercial fishing coexist alongside more investments in data collection to address scientific uncertainty can help provide clearer management guidance to meet harvest needs of both sectors and allow for better policies and governance of the 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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".