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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".