Compensatory responses by managers, commercial and recreational harvesters to variation in stock abundance of Lake Erie walleye ( <i>Sander vitreus vitreus</i> )
Bibliographic record
Abstract
Abstract Delayed quota adjustments, and/or lagged fishing effort and catch by harvesters, to changes in stock abundance may induce unstable population dynamics and exacerbate the risk of fishery collapse. We examined a 39-y time series of change to quotas by managers, and to effort and catch by both commercial harvesters and anglers, in response to changes in Lake Erie walleye abundance ( Sander vitreus ) estimated both contemporaneously and retrospectively. Quotas, commercial effort and catch were entrained by contemporaneous estimates of stock abundance. Recreational effort and harvest were not; they had better tracked abundance, as better estimated today, than did the commercial fishery. During the 1990s, a significant mismatch developed between the quota-driven commercial harvest and stock abundance that persisted until a new assessment process obtained. The quasi-open access recreational fishery, instead, freed anglers to respond better to stock abundance. Further elaboration of adaptive risk governance processes, including multi-model inference for stock assessments, may bode well to further reduce risk to fisheries imposed by lagged adjustments to variation in stock abundance.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".