Factors influencing trends of polychlorinated naphthalenes and other dioxin-like compounds in lake trout (<i>Salvelinus namaycush</i>) from Lake Ontario, North America (1979–2004)
Bibliographic record
Abstract
Concentrations of polychlorinated naphthalenes (PCNs) were determined in archived lake trout (Salvelinus namaycush) from Lake Ontario, North America, collected between 1979 and 2004 to evaluate their temporal trends and the factors influencing their trends. Concentrations of PCNs, as well as polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), and non- and mono-ortho-substituted polychlorinated biphenyls (DL-PCBs), which were measured for comparative purposes, declined by eight-, seven-, and fivefold, respectively, between 1979 and 2004. Apparent elimination rate constants (k2) were calculated as the slopes of the regression lines of concentration versus time for PCN, DL-PCB, and PCDD/F congeners to compare the rate of decrease among congeners within and between compound classes. The k2 values for PCNs that had two pairs or three adjacent carbons unsubstituted with chlorine (congeners that can be biotransformed by vertebrates) were not significantly different from zero, indicating no decline in fish. For PCN congeners having no adjacent carbons unsubstituted with chlorine, the k2 values generally increased with hydrophobicity and degree of chlorination. This pattern differed from that of PCDD/Fs and DL-PCBs and from previous findings for non-DL-PCBs, for which the rate of contaminant decline decreased with hydrophobicity, and the pattern also differed from expectations based on thermodynamics. Differences in the rate of decline of PCN congeners may be caused by changes in source or mixture formulations over time and/or metabolic dechlorination of the less stable, higher-chlorinated PCNs 73, 74, and 75 to lower-chlorinated congeners. Based on suggested dioxin toxic equivalency factors, PCN concentrations in these whole lake trout may be sufficient to trigger consumption restrictions in Ontario, Canada, and our results suggest that PCNs merit incorporation into monitoring and assessment programs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".