Quantifying inter-population variability in yellow perch sexual size dimorphism
Bibliographic record
Abstract
Size-selective angler behavior or the implementation of length-based regulations may have implications in harvest-oriented yellow perch (Perca flavescens) fisheries where perch populations may display sexual size dimorphism (SSD), even though the occurrence of SSD remains poorly documented. Predicted and observed mean lengths-at-age were used to describe the occurrence of yellow perch SSD in populations from four states and one Canadian province. In addition, abiotic and biotic variables were used to predict the occurrence of SSD in yellow perch populations. Predicted mean lengths-at-age were significantly greater for female yellow perch after age 3 indicating female-biased SSD (higher female growth rates and greater maximum attainable lengths) occurred upon maturity. Using observed mean lengths-at-age, 85% of the study populations had at least 1 year class where females were significantly larger than males. Female-biased SSD was present in almost two-thirds of the individual observed mean length-at-age year class comparisons. SSD could not be reliably predicted using yellow perch population dynamics or lake morphometry. Although, yellow perch SSD was positively correlated with lake productivity, a low fit statistic suggests a poor predictive relationship. This study has demonstrated the prevalence of female-biased SSD in yellow perch populations. Because yellow perch anglers are size selective and harvest oriented, the occurrence of female-biased SSD in a perch population will likely result in female-biased exploitation and, therefore, we recommend natural resource agencies collect age and gender-specific data to identify the occurrence of SSD in perch populations.
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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 | 0.001 |
| 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".