Eroding productivity of walleye populations in northern Wisconsin lakes
Bibliographic record
Abstract
Managing fisheries through rapid environmental change requires diverse approaches for identifying and adapting to novel ecological conditions. For the Wisconsin Ceded Territory, we calculated 473 adult walleye (Sander vitreus) production (P), biomass (B), and P/B estimates for 1990–2012. Frequency distributions for production statistics were right-skewed, indicating the fishery is generally dominated by low production populations. Mean P, B, and P/B were significantly elevated in natural recruitment (NR) lakes compared with combination (NR + stocking) and stocked-only lakes. Furthermore, combination populations had significantly higher production compared with stocked-only lakes. In NR lakes, walleye productivity changed little over time; however, the proportion of NR populations has declined over time. In combination and stocked-only populations, there were significant temporal declines in P, B, and P/B, and the proportion of these lakes has increased through time. This study reveals the crucial link between fish recruitment potential and fish production, helping to explain why the regional walleye fishery is struggling. Causes for walleye recruitment and production declines remain unclear, but long-term shifts in fish habitats are likely involved (e.g., from climate change and indirect food web effects). Decreasing walleye production is an important and emerging fishery management challenge in the region and portends a need to adapt fisheries management systems collaboratively for future sustainability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".