A social–ecological study of stock structure and fleet dynamics in the Newfoundland herring fishery
Bibliographic record
Abstract
Abstract Successful management of small pelagic fisheries is critical in integrated ecosystem based approaches and requires understanding of how the ecological dynamics of pelagic stocks mesh with the economic and social dynamics of commercial fisheries and the larger systems within which they operate. Combining insights from stock assessments with those from local fishers, scientists, and managers, can help identify knowledge gaps that could jeopardize stock resilience. This article presents results from a social-ecological, mixed-methods study that combines insights from science and from interviews with fishermen, scientists, and managers of small pelagic fisheries in western Newfoundland, Canada (NAFO division 4R) and in NAFO division 4X. Different approaches to herring management are used in the two areas. In area 4R fishing for herring (Clupea harengus) is part of a complex multi-species, multi-gear fishery; most harvesters who target herring also target Atlantic mackerel (Scomber scombrus). Harvester interviews indicate herring in 4R, like herring in 4X and elsewhere, have substantial within-species stock structure, but that it is not well-documented in science and not well protected under the current management system. Further, fishing strategies in the competitive mackerel fishery in which the herring vessels are involved may contribute to the risk of over-fishing on some herring populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".