Sex-based differences in spawning behavior account for male-biased harvest in Lake Erie walleye (<i>Sander vitreus</i>)
Bibliographic record
Abstract
Sex- and size-specific reproductive behaviors can increase the vulnerability of certain demographic components of fish populations to exploitation, potentially leading to unsustainable harvest. Lake Erie’s largest walleye (Sander vitreus) spawning population, which aggregates on the Ohio reef complex during spring, is subject to angling. Information on the sex composition of harvest or how reproductive behavior might influence harvest is lacking. To address these uncertainties, we implanted 337 reef-spawning individuals with acoustic transmitters, and their spawning behavior on the reef complex was monitored for 4 years using acoustic telemetry. Males arrived on spawning grounds earlier and remained on them longer than females. These behavioral differences led us to predict that recreational angler harvest during the spawning season would be male-biased. Creel surveys confirmed this prediction, although sex composition of the harvest was influenced by angling technique. Collectively, these findings suggest that sex-based differences in reproductive behaviors bias the recreational harvest toward males on the reef complex during the spawning season. This male-biased harvest seems unlikely to pose an undue risk to Lake Erie’s walleye fishery.
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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 | 0.000 | 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) | 0.002 | 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".