Influence of lake characteristics on the biomagnification of persistent organic pollutants in lake trout food webs
Bibliographic record
Abstract
The biomagnification of polychlorinated biphenyls (PCBs) and major organochlorine pesticides (OCPs) was studied using lake trout (Salvelinus namaycush) and other food web organisms collected from 17 lakes in Canada and the northeastern United States between 1998 and 2001. Whole lake trout (n = 357) concentrations of the sum (Sigma) of 57 PCB congeners ranged between 1.67 and 2,890 ng/g wet weight (median 61.5 ng/g wet wt). Slimy sculpin had the highest mean concentrations of SigmaPCB of all forage fish (32-73 ng/g wet wt). Positive relationships between log (lipid wt) concentrations of PCB congener 153, PCB congener 52, p,p'-dichlorodiphenyldichloroethylene, hexachlorobenzene, cis-chlordane, trans-nonachlor, or dieldrin and trophic level (determined using stable nitrogen isotope ratios) were found for most of the 17 food webs, indicating biomagnification of these PCBs and OCPs. The p,p'-dichlorodiphenyldichloroethylene had the highest trophic magnification factors (TMFs) of the 14 individual compounds studied, averaging 4.0 +/- 1.8 across the 17 lakes, followed by trans-nonachlor (3.6 +/- 1.5) and PCB congener 153 (3.4 +/- 1.2). Average TMFs for 14 individual PCBs or OCPs were significantly correlated with log octanol-water partition coefficient, implying that the rate of accumulation along the food web is dependent on hydrophobicity and recalcitrance. Significant correlations (p < 0.05) were found between TMFs of SigmaPCBs, hexachlorobenzene, alpha-hexachlorocyclohexane, and lindane and lake area, latitude, and longitude, but not for 11 other PCBs or OCPs. Overall, the results of the present study show that biomagnification of PCBs and most OCPs, as measured by TMFs, is only weakly influenced by such factors as latitude and longitude. Exceptions are hexachlorocyclohexane isomers and hexachlorobenzene, which had generally greater TMFs in northern lakes, possibly due to lower rates of elimination and biotransformation in the food web.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".