Selective fishing and shifting production in multispecies fisheries
Bibliographic record
Abstract
A limited ability to target or avoid individual stocks complicates successful output management in multispecies fisheries. For vessels in these fisheries, reducing harvest of one species often requires simultaneous reductions in harvest of other stocks. The extent to which multispecies allocation targets can be met may depend critically on harvesters’ ability to substitute production across species. We introduce a measure of compositional control that captures the level of forgone production resulting from imperfect selectivity. This metric is then applied to data from the New England multispecies groundfish fishery and used to test for evidence of limited selectivity in the composition of individual vessel daily landings. Results indicate that increases in landings of one species generally require simultaneous increases in landings of other species — a finding that suggests difficulty in substituting production across groundfish species. Our measure is seen to vary widely through time as well as across vessels and species and may be affected by both environmental conditions and incentives created through management. The model developed here should hold value for managers and researchers seeking to assess interstock economic trade-offs in multispecies fisheries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".