Criteria for a good catch: A conceptual framework to guide sourcing of sustainable salmon fisheries
Bibliographic record
Abstract
The identification of sustainably managed fisheries is problematic for marketers and consumers of Pacific salmon food products owing to lack of well-defined and robust criteria that take into account current ecosystem science of salmon. We present the rationale for an alternative conceptual framework for salmon management that supports the development of sustainable sourcing criteria. Our approach contrasts with current large-scale fisheries certification programs such as that of the Marine Stewardship Council (MSC) and general consumer recommendation services such as Monterey Bay Aquarium’s Seafood Watch (SFW) program. Our framework is based on the “place-based” character of salmon populations and recognition of fundamental aspects of salmon ecology, particularly the evolution of population life histories that are locally adapted to freshwater spawning and rearing habitats. We describe how this framework underpins development of science-based sourcing criteria and how it differs in important respects from the industrial approach that historically and currently is the basis for most salmon management. We conclude with a discussion of how the framework and its application may provide a model for redirecting salmon management, in general, towards a more science- and place-based approach and why that is likely to be sustainable in the long term in a way that most contemporary salmon management is not.
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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 | 0.045 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".