Small fish, big implications: considerations for an ecosystem approach to capelin fisheries management
Bibliographic record
Abstract
Climate-driven changes in the Subarctic will directly impact capelin populations and the ecosystem they inhabit, including their predators, prey, and physical habitats. Consequently, incorporating ecosystem considerations in future capelin fisheries management is crucial. In this study, a multidisciplinary group of experts critically evaluated whether the current capelin stock assessment and management frameworks for the four main capelin stocks in the Barents Sea (BS), Iceland-East Greenland-Jan Mayen (IEGJM), Newfoundland and Labrador shelf (NL) and Alaska (AK) align with the principles of an Ecosystem Approach to Fisheries Management (EAFM). An evidence-based ranking of our knowledge on current capelin dynamics across ecological, economic, and social dimensions was conducted, using expert knowledge supported by literature. This exercise also identified data currently used for assessment and management, which highlighted that the existing capelin assessment frameworks include varying degrees of EAFM elements across stocks, such as considerations of trophic interactions, bottom-up processes, accounting for ecosystem uncertainty, and stakeholder engagement in the advisory process. Nonetheless, there is room for improvement where data and knowledge are lacking. We provide some key tactical (short-term) and strategic (long-term) recommendations from our perspective on what is required to ensure the sustainable management of capelin in the circumpolar region over the coming decades.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".