Management strategy analysis for multispecies fisheries, including technical interactions and human behavior in modelling management decisions and fishing
Bibliographic record
Abstract
A multispecies fishery management strategy evaluation (MSE) framework based on the example of the groundfish fishery in the Bering Sea and Aleutian Islands region of Alaska was used to examine the interplay between a bycatch species and three groundfish species. The study introduces a framework for a realistic multispecies fishery MSE by accounting for fleet dynamics, multispecies fishery quota allocation, and the temporal dynamics of technical interactions. The quota allocation and the fleet dynamics models were implemented using linear programming, and regression approaches were used to make a realistic projection of future users’ behavioral response to changes in the fishery. The models were calibrated and then validated using historical and out-of-sample data, respectively. The results highlight the importance of accounting for technical interactions and their interannual dynamics for both quota allocation and fleet dynamics to design a realistic multispecies fishery MSE (without them, the amount of lost yield increased). Therefore, particular attention should be paid to understanding human behavior as well as its uncertainty and to refining approaches to incorporate this information into a multispecies fishery management strategy analysis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".