PCB Elimination by Yellow Perch (<i>Perca flavescens</i>) during an Annual Temperature Cycle
Bibliographic record
Abstract
The significance of temperature on aquatic species ecology and physiology is well recognized yet its effects on chemical bioaccumulation kinetics are less well understood under natural conditions. In this study, yellow perch were dosed with a polychlorinated biphenyl (PCB) mixture and allowed to depurate the chemicals over 1 year under an ambient temperature cycle characteristic of northern temperate latitudes. PCB elimination kinetics during the summer months at optimal water temperature for perch (23 degrees C) were similar to those observed in lab studies with other species reared at their optimal temperature. During the fall and winter seasons, however, elimination of only 11 PCB congeners of log K(ow) < or = 5.7 was observed and half-lives averaged > 1000 d for these PCBs. PCB elimination was again observed with the onset of spring temperatures but elimination rates averaged 2.6 times slower for readily metabolized congeners and 7.5 times slower for more persistent PCBs than observed during the summer. Bioenergetics modeling efforts predicted maximum values for respiration, fecal egestion, and growth rates during summer months but also predicted rapid declines in these chemical dilution processes during the fall and winter concurrent with changes in temperature. As temperature increased into the spring, bioenergetic rates were predicted to increase but only achieved approximately 85% of maximum rates predicted for summer peak temperatures. These results indicate that minimal chemical elimination occurs in perch when metabolic functioning falls to low maintenance levels during the fall and winter. These seasons encompass approximately 8 months of the year at northern temperate latitudes and therefore these patterns have significant consequences for understanding mechanisms of food-web biomagnification of hydrophobic organic chemicals in aquatic systems.
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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 | 0.000 | 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) | 0.000 | 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".