Sex-specific covariation among life-history traits of yellow perch (Perca flavescens)
Bibliographic record
Abstract
Questions: How do life-history traits covary among populations? Do two-trait models show different patterns of covariation than multi-trait models? Is covariation different for males and females? Is covariation among traits within populations (many generations) different to that among populations (one generation)? Organism: A sexually dimorphic medium-sized freshwater fish, yellow perch (Perca flavescens). Study system: Over 70 lakes in central North America. Methods: Fish older than young-of-the-year were collected using standardized autumn surveys. Mean life-history traits were calculated for each population by sex. Conclusions: Life-history traits generally covaried in the predicted manner among populations. Traditional two-trait comparisons resulted in similar conclusions as more complex models of covariation. Male and female patterns of covariation differed substantially for relationships between growth and age/size at maturation, moderately for lifespan and age at maturation, but were similar for size at maturation and maximum size. The relationships between female growth rate and maturation depended on the cause of variability in growth rates. Slow-growing populations matured young and small in warm lakes but old and large in cold lakes. Patterns of covariation in life-history traits were similar for temporal and spatial variability in traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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; both teacher heads agree on what is shown here.
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".