Evaluation of Daily Creel and Minimum Length Limits for Black Crappie and Yellow Perch in Wisconsin
Bibliographic record
Abstract
Abstract Harvest regulations for Black Crappie Pomoxis nigromaculatus and Yellow Perch Perca flavescens in the northern USA and Canada have not been thoroughly evaluated, and specific guidance regarding where minimum length limits (MLLs) might improve these fisheries is lacking. We examined whether: (1) transitioning from an aggregate statewide daily creel limit of 25 panfish to species-specific daily creel limits of <25 fish or implementing statewide MLLs could reduce harvest of Black Crappie and Yellow Perch in Wisconsin by ≥25%, and (2) MLLs would improve yield by ≥10% and mean TL of harvested fish by ≥25 mm in Wisconsin fisheries. Creel surveys indicated that ≥94% of Wisconsin anglers did not harvest a Black Crappie or Yellow Perch, and ≤0.12% of anglers harvested a daily creel limit of 25 fish. Daily creel limits would need to be ≤7 fish/ angler to reduce harvest by ≥25%. Statewide MLLs would need to be ≥229 mm for Black Crappie and ≥203 mm for Yellow Perch to reduce harvest by ≥25%, but predicted responses to MLLs varied among simulated populations. In general, MLLs were not predicted to improve yield, indicating that growth overfishing was not a widespread problem. Minimum length limits could improve mean TL of harvested fish, but increases ≥25 mm were only observed under 254-mm and 279-mm MLLs, and anglers would have to accept predicted reductions in harvest of ≥30% to achieve these improvements. A 229-mm MLL offered a more equitable trade-off between increases in mean TLs of harvested fish (11–21-mm improvements) and reductions in harvest (22–37% reductions). Our modeling provides a framework for managers to make more informed decisions regarding harvest regulations, but more information regarding angler preferences is needed for selecting appropriate management objectives and harvest regulations. Received June 11, 2014; accepted September 4, 2014
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".