Designing tools to evaluate fishery management strategies: can the scientific community deliver?
Bibliographic record
Abstract
Abstract Schnute, J. T., Maunder, M. N., and Ianelli, J. N. 2007. Designing tools to evaluate fishery management strategies: can the scientific community deliver? – ICES Journal of Marine Science, 64: 1077–1084. Techniques for quantitative fishery management have evolved rapidly during a period when computers, programming languages, and computational algorithms have also changed dramatically. Despite these advances, many stock assessment methods remain untested. A process of management strategy evaluation (MSE) could potentially rectify this problem, but it would require a framework in which to conduct systematic tests. We survey the tools currently used for stock assessments and discuss the development of new standards for testing management procedures. A successful project would depend on human skills scattered among various nations, organizations, and academic disciplines. Analogies from civil engineering illustrate the discipline and collaboration required for an effective outcome. If the world community of fishery scientists could design, build, and support such a project, it would revolutionize the theory, teaching, and practice of scientific fishery 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 | 0.385 | 0.617 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.028 | 0.031 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".