A mixed-species yield model for eastern Bering Sea shelf flatfish fisheries
Bibliographic record
Abstract
A variety of eastern Bering Sea (EBS) flatfish including yellowfin sole (Limanda aspera), rock sole (Lepidopsetta bilineata), flathead sole (Hippoglossoides elassodon), and Alaska plaice (Pleuronectes quadrituberculatus), co-occur in various degrees in EBS trawl fisheries, impeding attempts to obtain single-species management targets. A further complication is the bycatch of Pacific halibut (Hippoglossus stenolepis); halibut bycatch limits, rather than single-species catch quotas, have been the primary factor regulating EBS flatfish harvest in recent years. To examine bycatch interactions among the EBS flatfish listed above, an equilibrium mixed-species multifishery model was developed. Equilibrium yield curves, scaled by recent average recruitment, are flat topped or asymptotically increasing, reflecting low fishing selectivity during the first several years of life and low growth relative to natural mortality. A linear programming analysis indicated that relaxation of the halibut bycatch constraint at the optimal solution of catch by fishery would produce approximately 20 times more flatfish yield than a similar relaxation of any flatfish catch quota. A strategy for establishing halibut bycatch limits that considers the foregone revenue in the halibut and flatfish trawl fisheries reveals how the choice of halibut bycatch limit is affected by the management goal for the flatfish complex.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".