“Satisficing” and trade-offs: evaluating rebuilding strategies for Greenland halibut off the east coast of Canada
Bibliographic record
Abstract
Abstract Miller, D. C. M., and Shelton, P. A. 2010. “Satisficing” and trade-offs: evaluating rebuilding strategies for Greenland halibut off the east coast of Canada. – ICES Journal of Marine Science, 67: 1896–1902. To be effective, management strategy evaluation (MSE) requires a well-defined procedure for comparing the merits of candidate management strategies. We explore a two-step approach of “satisficing” followed by a trade-off analysis. “Satisficing” (a portmanteau of “satisfy” and “suffice”) is a decision-making procedure that attempts to meet criteria for adequacy, rather than identify an optimal solution. As a case study, we consider the results from a comprehensive MSE for Greenland halibut off the east coast of Canada, carried out under the auspices of the Northwest Atlantic Fisheries Organization. First, we apply satisficing to the results to determine which rebuilding strategies achieve pre-specified thresholds set for imperative performance statistics relating to resource conservation, yield, and stability of the fishery. Next, trade-offs among important, but not necessarily imperative, performance statistics are evaluated for those strategies that pass the satisficing step. For Greenland halibut, a management strategy containing a simple feedback harvest-control rule based on recent trends in survey estimates of abundance satisfices all imperative requirements and provides the best trade-off in other performance statistics. The approach necessitates translating objectives for stock rebuilding and sustainable fisheries into operationally explicit terms and incorporates a priori consideration of stakeholders' concerns.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".