The dual role of indicators in optimal fisheries management strategies
Bibliographic record
Abstract
Abstract Rice, J. C., and Rivard, D. 2007. The dual role of indicators in optimal fisheries management strategies. – ICES Journal of Marine Science, 64: 775–778. Indicators are used in two different ways in the assessment and advisory cycle. One is to audit performance of the management plan relative to achieving the objectives for the fishery. The second is to trigger control rules to manage the subsequent harvest. Traditionally, the assessment and management community has used spawning-stock biomass and fishing mortality for these functions, and as management strategies are being developed, generally continues to test the same indicators in both the audit and control functions. There is no reason to use the same indicators in both functions, and management of a few specialized commercial fisheries has recognized this, using different indicators in different roles for many years. That different indicators may be optimal for both roles presents a richer range of opportunities for exploring robust management strategies, and will be essential as ecosystem considerations and integrated management tools are included in assessment and management.
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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 | 0.040 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".