Sex-based trade-offs among growth, mortality, and maturation in Great Lakes yellow perch stocks
Bibliographic record
Abstract
Trade-offs among growth, mortality, and reproduction form the basis of life history theory but may vary among populations owing to local ecological conditions. We examined life history trade-offs driving variation in maturation among 13 yellow perch (Perca flavescens) stocks in the Great Lakes using sex-specific age and length at 50% maturity (A50 and L50, respectively) and probabilistic maturation reaction norm midpoints (Lp50,a). Both sexes exhibited positive correlations between growth and mortality, and faster-growing stocks were mature at younger ages but larger sizes. Male and female A50 and L50 were positively correlated among stocks, but Lp50,a estimates were negatively correlated among stocks, indicating stocks that matured at large sizes for a given age in females matured at smaller age-specific sizes in males. Female Lp50,a estimates were negatively related to growth and mortality, while male Lp50,a estimates were positively related to growth. These results suggest that (i) sex-based life history trade-offs sometimes act to differentially structure maturation schedules in males and females and (ii) males may be less responsive to changes in mortality than females.
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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 | 0.001 | 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".