Lower possession limits and shorter seasons directly reduce for-hire fishing effort in a multispecies marine recreational fishery
Bibliographic record
Abstract
Managers of recreational fisheries often rely on implicit and rarely tested assumptions regarding how fishing effort will change in response to regulations. For instance, they assume that reduced seasons will directly reduce fishing effort without producing angler behavioral adaptations to maintain fishing opportunities and harvest. Vessel trip reports from a multispecies for-hire fishery in New Jersey, USA, allowed us to empirically evaluate changes in fishing effort as overlapping seasons for four species became shorter and as possession limits decreased. We conducted focus groups with fishery stakeholders and then developed statistical models to evaluate hypotheses describing how anglers aboard for-hire vessels adapted to regulations. Fishing effort aboard charter boats remained consistent and primarily responded to the availability of “something” to harvest, suggesting that their customers are willing to substitute target species. Party boat anglers, in contrast, responded to the possession limits of black sea bass (Centropristis striata) and summer flounder (Paralichthys dentatus). Because party anglers were less willing to substitute target species, party vessel operators are likely particularly vulnerable to reductions in fishing opportunity and harvest potential.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".