Predicting walleye recruitment as a tool for prioritizing management actions
Bibliographic record
Abstract
We classified walleye (Sander vitreus) recruitment with 81% accuracy (recruitment success and failure predicted correctly in 84% and 78% of lake-years, respectively) using a random forest model. Models were constructed using 2779 surveys collected from 541 Wisconsin lakes between 1989 and 2013 and predictor variables related to lake morphometry, thermal habitat, land use, and fishing pressure. We selected predictors to minimize collinearity while maximizing classification accuracy and data availability. The final model classified recruitment success based on lake surface area, water temperature degree-days, shoreline development factor, and conductivity. On average, recruitment was most likely in lakes larger than 225 ha. Low degree-days also increased the probability of successful recruitment, but primarily in lakes smaller than 150 ha. We forecasted the probability of walleye recruitment in 343 lakes considered for walleye stocking; lakes with high probability of natural reproduction but recent history of recruitment failure were prioritized for restoration stocking. Our results highlight the utility of models designed to predict recruitment for guiding management decisions, provided models are validated appropriately.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".