Evaluating regional length limits in freshwater fisheries
Bibliographic record
Abstract
Length limits are often used in recreational fisheries management to prevent overharvest and manipulate fish size distributions. These regulations are ideally customized to meet water-specific stock dynamics and fishery objectives. However, in districts with numerous discrete waters, fisheries are commonly managed with a universal regional regulation. Evaluating alternative regional length limits requires consideration of management objectives that may not be important at the single-system level but that emerge as relevant at the regional scale, such as uniformity of regional harvest, diversity of average catch sizes, and opportunity to harvest. We developed a flexible tool for evaluating regional length limits. The tool joins the well-established Beverton–Holt yield-per-recruit model with elements of decision-support methods. The model quantifies regional management objectives as utility functions that are weighted and summed into a single value used to evaluate alternative length limits. The flexibility of the tool stems from its capacity to consider a mixture of stock parameters and associated uncertainty to evaluate multiple length limits, weighting an array of regional fishery objectives quantified by various performance metrics. This adjustability affords flexibility to consider a diversity of options that can stimulate innovation in setting regional length limits. We demonstrate the model by evaluating varying length limits on fishery objectives related to the management of hypothetical yellow perch ( Perca flavescens) populations and real black crappie ( Pomoxis nigromaculatus) populations.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".