Synchronous changes in length at maturity across four species of Lake Erie fish with different harvest histories
Bibliographic record
Abstract
Decreases in size at maturation in harvested fish populations can reduce productivity and resilience. Delineating the causes for these changes in maturation is challenging. We assessed harvest and large-scale ecosystem variability as causes for changes in maturation in four Lake Erie fishes. Regulated harvests of yellow perch (Perca flavescens) and walleye (Sander vitreus) are greater than unregulated harvests of white perch (Morone americana) and white bass (Morone chrysops). Our assessment considered cohort data from 1991 to 2012 for each species. We used a conceptual model of harvest-induced plasticity to show that changes in female length at 50% maturity (L 50) were unrelated to harvest intensity in all species. We then demonstrated that changes in female L 50 among cohorts were synchronous across species. Post hoc analysis of variables capturing year-to-year variation in climatic and lake conditions suggested L 50 was larger when water levels were near the norm for the study period and smaller at low and high levels. We conclude that changes in L 50 were most strongly related to ecosystem changes unrelated to harvest intensity.
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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 | 0.001 |
| 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 | 0.001 |
| 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".