A Simulation Model to Assess Management and Allocation Alternatives in Multi-Stock Pacific Salmon Fisheries
Bibliographic record
Abstract
A fisheries simulation model was developed to evaluate different management regimes on multiple salmon stocks harvested by multiple fisheries. The model also assesses the economic effects on individual and collective fisheries. The model is tested using the sockeye salmon Oncorhynchus nerka and pink salmon O. gorbuscha fisheries of northern British Columbia and southern Southeast Alaska. Four stock groups (U.S. and Canadian pink and sockeye stocks) and 12 fisheries (five Alaskan and seven Canadian intercepting and terminal commercial fisheries) are included. Fishing effort, in terms of harvest rate for these simulations, is the exogenous variable and stock size and net economic benefit over time are among the output variables. Criteria were developed to reflect different management schemes; given the criteria, the model simulates the effort needed in each fishery to implement the management policy. Three management schemes were assessed for this study: maximizing sustainable yield of the stocks, balancing interceptions by the two countries, and maintaining a fixed harvest rate per fishery. The simulation model suggests that stock production and economic benefits to the fisheries may be reduced significantly when the two countries allocate according to a system that equalizes fishery interceptions rather than maximizes the size of the aggregate harvest.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".