Efficacy of harvest and minimum size limit regulations for controlling short‐term harvest in recreational fisheries
Bibliographic record
Abstract
Abstract It is important to consider the potential effectiveness of regulations for reducing total harvest levels when developing fishery management plans. A random forest ( RF ) modelling approach was used to examine how changing per‐angler harvest or minimum size limit regulations affected sport fishery harvest in US Atlantic coast recreational fisheries. Harvest limits per angler (i.e. bag limits) were typically high initially and subsequently reduced, whereas almost half of minimum length limits were initially below the length‐at‐maturity and subsequently increased. Across most fisheries examined, extreme reductions in harvest limits (e.g. from unlimited to catch‐and‐release) were largely ineffective at limiting total fishery harvest. Increasingly restrictive minimum length limits caused a greater average harvest reduction than per‐angler harvest limits. Some regulation changes were associated with higher angling effort and thus increased harvest, which suggests that when effort cannot be constrained, more direct harvest limitations should be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".