Fisheries Systems Models For Evaluating Alternative Management Strategies: Six Oecd Case Studies
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.In 1997, the OECD commissioned a series of fisheries case studies to examine the transition to a more “responsible” status as defined in general terms by the FAO’s Code of Conduct for Responsible Fisheries. These cases studies included a cross section of fisheries (groundfish, small pelagics, and invertebrates) from OECD member countries: Canada, Iceland, Australia, New Zealand, Germany, and Japan. The analyses of these case studies consisted of an annual historical, current, and projected status of the fisheries of interest with respect to integrated targets for: biological, economic, social, and administrative criteria. The case models were developed using the spreadsheet program Excel with a menu-based interface for ease of use. The results provide users with the capabilities to evaluate the impacts of alternative policy strategies including changes in resource global annual catch limits, changes in allocations to gear sectors, and changes in fishing fleet structure including assumptions of rationalization under quota systems. Model evaluation of policy alternatives includes both a deterministic and a stochastic framework with randomness attributed primarily to stock recruitment and growth, and economic market and cost variables. The presentation will discuss (1) the multicriteria modeling framework for evaluating the performance of the cases under selected policy adjustment, (2) the analysis feedback process for policy selection and evaluation, and (3) the principle results and conclusions arising from the various case studies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".