Polychlorinated dibenzo-<i>p</i>-dioxins and dibenzofurans and dioxinlike polychlorinated biphenyls in sediments and mussels at three sites in the lower Great Lakes, North America
Bibliographic record
Abstract
Levels of contaminants including polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), non-ortho-substituted and mono-ortho-substituted dioxinlike polychlorinated biphenyls (DLPCBs) were determined in sediments and freshwater mussels (Dreissena spp. and Elliptio complanata) at three sites in the lower Great Lakes (North America). Impacts of mussel colonization on sediment quality were investigated by comparing contaminant levels in colonized sediment with levels in sediment in the same area that was not colonized, but exposed to similar environmental conditions. Significant impacts on contaminant levels of colonized sediment, compared to noncolonized sediment, were observed at two sites exhibiting high mussel population densities (Fort Erie, eastern Lake Erie, ON, Canada, 2.2 kg/m2 dry wt biomass, and Port Dalhousie, western Lake Ontario, Ontario, Canada, 6.1 kg/m2 dry wt biomass); these differences were not observed at a site with lower mussel densities (Bay of Quinte, eastern Lake Ontario, Ontario, Canada, 0.7 kg/m2). Total organic carbon and contaminant concentrations were statistically significantly greater in colonized sediment, compared to noncolonized sediment, at the two impacted sites. Areal estimates of PCDD/PCDF and DLPCB toxicity equivalents (TEQs) in mussel biomass at the three sites averaged 0.16% and 3.3%, respectively, of the TEQs in the top 3 cm of sediment, indicating that the sediments were the primary sink for contaminants. The observed differences between colonized and noncolonized sediment suggest that Dreissena are capable of influencing the chemical properties of sediment they colonize.
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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 | 0.001 |
| 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".