Maternal effects better predict walleye recruitment in Escanaba Lake, Wisconsin, 1957–2015: implications for regulations
Bibliographic record
Abstract
Maternal influences on age-0 walleye (Sander vitreus (Mitchill, 1818)) recruit abundance and survival from egg to fall were observed in Escanaba Lake, Wisconsin, in 1957–2015. Annual egg production best explained variation in age-0 recruitment, compared with female relative abundance, and adult abundance (sexes combined). Age-0 recruitment was not significantly correlated with any temperature metric tested or our index of yellow perch (Perca flavescens (Mitchill, 1814)) abundance. Survival of walleye from egg to fall age-0 was positively correlated with the percent contribution of large females (>55.9 cm) to annual egg production. Mean size diversity of females by length class did not influence age-0 recruit abundance or survival over time. Evidence for maternal effects via size- and age-specific influences on fecundity and age-0 walleye survival suggest that exploitation may influence natural recruitment by altering adult female size structure. Given recent declines observed in walleye natural recruitment in the upper Midwestern USA, understanding the roles of maternal drivers and exploitation on recruitment is critical for sustainable walleye management.
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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".