Enzymatic correlates of energy status in wild yellow perch inhabiting clean and contaminated environments
Bibliographic record
Abstract
Enzymes representing a variety of metabolic pathways were examined in yellow perch (Perca flavescens) collected from a metal-contaminated region (Rouyn-Noranda, Québec, Canada) to determine which were most closely related to fish condition factor, pyloric caeca weight, and visceral lipid accumulation, as well to seek a better understanding of the influence of metal contamination on the physiology and biometrics of perch. Compared to laboratory fish, wild perch were under important energy restrictions. The condition factor of wild fish was correlated with indicators of aerobic metabolism (citrate synthase, cytochrome C oxidase), protein anabolism (nucleoside diphosphokinase), and indicators of lipid accumulation (glucose-6-phosphate dehydrogenase, visceral lipid index). Pyloric caeca weights were well correlated with indicators of protein anabolism, but only when both seasons were examined together, possibly indicating a lag in the response of enzymes to changes in diet. The addition of contaminant stress to existing energy restrictions led to changes in the relationships between enzymes and biometrics, reducing the predictive power of the models for perch in contaminated lakes. The present study broadens our knowledge of the impact of metal contamination on energy accumulation and tissue metabolic capacities in wild perch.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".