Management of fisheries on forage species: the test-bed for ecosystem approaches to fisheries
Bibliographic record
Abstract
Abstract Rice, J., and Duplisea, D. 2013. Management of fisheries on forage species: the test-bed for ecosystem approaches to fisheries. – ICES Journal of Marine Science, 71: . In the 1970s and 1980s, core ideas about management of fisheries on forage species emerged from work on the dynamics of foodweb models and multispecies assessments, leading to proposals for management that took some account of the role of forage species in marine ecosystems. Key developments in those years are summarized in the first part of this paper. From the 1980s to the 2000s, studies of the response of forage species to environmental variation brought into question the robustness of management strategies for forage species. As a result, additional management strategies were proposed to accommodate environmental drivers as well as dependent predators. The paper reviews these developments. This paper brings these separate lines together in a systematic framework for evaluating the performance of six different management strategies for forage species, relative to four different ecosystem considerations, as well as relative to the contribution of forage fisheries to economic prosperity and food security. The tabulated outcomes synthesize primary and secondary literature and meeting deliberations as the application of an ecosystem approach to management has evolved. No strategy is optional for all forage fisheries. As experience accumulates, the guidance in the tables comprising the framework will improve.
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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 | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".