Size‐ and Sex‐Specific Capture and Harvest Selectivity of Walleyes from Tagging Studies
Bibliographic record
Abstract
Abstract Estimates of size‐ and sex‐specific gear selectivity are important for making informed management decisions. Sex‐specific selectivity curves may be needed for two‐sex statistical catch‐at‐age models when information about sex ratios in the catch is unavailable. We used data from three tagging programs in Minnesota and Wisconsin to estimate the size‐ and sex‐specific selectivity of angling and spearing for Walleyes Sander vitreus. We estimated capture selectivity (the relative catchability of each component of the population) and harvest selectivity (the combined effect of capture selectivity and the decision to retain or release a fish from a given component). These components are of interest because (1) the hooking mortality of released fish contributes substantially to total mortality, so that it is important to know how harvest and release vary by size; and (2) capture selectivity is likely similar across lakes, such that data from other lakes may provide information on capture selectivity for the lake of interest, while harvest selectivity is lake specific. Estimates were obtained using generalized linear models to determine the significance of the individual and interactive effects of length and sex on selectivity. Angling capture and harvest selectivity were both greater for females than males of every length. In contrast, spearing harvest selectivity was greater for males. For both sexes, harvest selectivity for angling and spearing peaked at around 400–450 mm. The capture selectivity of anglers peaked at 350–375 mm. The interaction between sex and size was significant for capture selectivity for angling, with the sex effect for small fish being less than that for large fish. Above 400 mm, spearing selectivity did not appear to vary with length for either sex, but at lengths below that it was lower for males.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".