Exploring optimal walleye exploitation rates for northern Wisconsin Ceded Territory lakes using a hierarchical Bayesian age-structured model
Bibliographic record
Abstract
We assessed population dynamics of walleye (Sander vitreus) in multiple Ceded Territory lakes, which support recreational and tribal fisheries, using a hierarchical Bayesian age-structured model. We used distributions of parameter estimates to develop a dynamic simulation model to forecast performances of walleye fisheries across these lakes under alternative recreational and tribal fishing scenarios. Application of a hierarchical approach allowed us to obtain more accurate estimates of stock–recruitment relationships, natural mortality, maturity and selectivity schedules, and growth parameters for individual lakes, especially for those with relatively uninformative data, and to characterize their variability among lakes. Using standing spawning stock biomass, recreational and tribal harvest, and probability of population collapse as performance metrics, our simulations suggest that northern Wisconsin walleye populations can sustain a regional optimal exploitation rate of about 20% on average given the existing recreational and tribal gear selectivities. However, lake-specific optimal exploitation rates may be higher or lower depending on estimated lake productivities, suggesting that effective management of the Ceded Territory walleye fisheries should account for variability in population dynamics among lakes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".