A Combined Food Web Toxicokinetic and Species Bioenergetic Model for Predicting Seasonal PCB Elimination by Yellow Perch (<i>Perca flavescens</i>)
Bibliographic record
Abstract
A commonly used toxicokinetic model was coupled to a bioenergetic submodel optimized for yellow perch and an empirical growth submodel to predict daily PCB elimination flux in three size classes of fish under seasonally variable temperatures. Across seasons, the bioenergetic model predicted highly variable gill ventilation and fecal egestion rates which varied by 74.2-111.2 fold and 35-65 fold, respectively, over the annual cycle. The empirical growth model accounted for seasonal trends in overwintering lipid losses evident for all fish size classes and growth, evident only for the small fish size class, during warm water periods. The toxicokinetic model described seasonal trends in congener specific PCB mass balance of fish, but tended to overestimate PCB elimination for less hydrophobic congeners when the recommended gill transfer efficiency term (Ew) of 0.54 was used. Downward adjustment of Ew to an average value of 0.14 produced the strongest model fit for several low Kow PCBs but had less effect on model performance for mid- to high Kow congeners. The toxicokinetic model was less sensitive to parameters involved in fecal elimination of PCBs when applied to low and mid-Kow PCBs. This study demonstrates the importance of seasonal trends in metabolic rate, growth, and overwintering weight loss as factors that modify PCB toxicokinetics in temperate fish.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".