Rapid changes in age and size at maturity in Lake Erie yellow perch (<i>Perca flavescens</i>) are not explained by harvest
Bibliographic record
Abstract
Harvest can change phenotypic traits of populations through immediate demographic consequences, evolutionary responses to harvest selection, or developmental responses by individuals. This study investigated the plastic phenotypic effects of harvest on size and age at maturity in a commercially exploited freshwater fish. We tested an individual growth and life history plasticity model using lagged correlations incorporating how harvesting fish ages 2 and older influenced the abundance of juvenile fish, resource availability, individual growth rates, and carry-over responses in age and size at maturity. Our test used cohort data for Lake Erie yellow perch (Perca flavescens). Age and size at maturity fluctuated widely and rapidly across 23 cohorts between 1991 and 2013, suggesting phenotypic plasticity contributed strongly to maturation dynamics. The changes in maturity could not be explained by responses to harvest, as expected under the plasticity model. In Lake Erie, age and size at maturity in yellow perch appear to be responding to other drivers, such as harvest-induced dynamics of other fish stocks or ecosystem changes that are independent of harvest.
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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 | 0.000 | 0.001 |
| 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".