Bibliographic record
Abstract
Walleye/ogaa ( Sander vitreus (Mitchill)) (hereafter, walleye; ogaa = Ojibwe translation) populations have historically supported important multi-use, harvest-oriented fisheries. Despite intensive management, walleye populations have declined in the midwestern United States raising concerns about the sustainability of the species. Numerous factors have been implicated in walleye population declines, including climate change, habitat loss, invasive species, species-interactions, production overharvest (i.e., harvest consistently exceeding annual production), and changing angler behaviors. These factors have negatively influenced natural recruitment and contributed to depensatory recruitment dynamics. I provide a review and perspective suggesting that the current trajectory of walleye populations is at or nearing an ecological tipping point. Although fish populations are often considered compensatory (i.e., negatively density-dependent), current walleye populations appear prone to depensation (i.e., positive density dependence). My review and perspective suggest that a compensatory management perspective for walleye is misaligned. A change in management towards a depensatory resource focus using ecosystem-based fisheries management and the recognition of walleye fisheries as social–ecological systems is needed for conservation. If compensatory management ensues, walleye persistence will likely be further threatened because many drivers of change are outside of managerial control, and those commonly used within managerial control have seemingly been ineffective for sustaining or rehabilitating naturally reproducing walleye 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".