Temporal Trends and Spatial Variabilities of PCB concentrations in Lake Trout from Lake Superior from 1995 to 2013
Bibliographic record
Abstract
It has been frequently reported that concentrations of PCBs in the Great Lakes fish have declined dramatically since their ban on production and use in 1979 in the United States, although some studies suggested that recent rates of decline are leveling off. In order to examine the temporal trends and spatial variabilities of PCB concentrations in lake trout (Salvelinus namaycush) from Lake Superior during the past two decades, statistical analyses were performed on fish sample data collected by two national agencies (U.S. EPA, Environment and Climate Change Canada) and three state agencies (Michigan Department of Environmental Quality, Minnesota Department of Natural Resources, Wisconsin Department of Natural Resources) from both the United States and Canada from 1976 to 2013. Because of a change in PCB analytical methodology in the mid-1990s, intercomparison between data recorded by the previous technique and the improved technique is not feasible. Because most organochlorine compounds are easily bound to fatty tissues in fish, lipid content has been commonly considered as a predictor of PCB levels. Also, larger fish were assumed to have higher PCBs in their bodies. Multiple linear regression analyses, setting time, lipid content, and fish length as three independent variables, revealed that lipid content had little impact on PCB concentrations at all sites except Whitefish Bay since 1995, which is in contrast to some previous studies. However, a strong positive correlation between PCBs and fish length, in good agreement with previous research, was observed at all sites except Whitefish Bay over the same period. It has been discovered that PCB concentrations vary among several sampling locations within Lake Superior. The general pattern was that the western sites had significantly higher concentrations than the eastern sites. When the entire historical record was analyzed, temporal trends were evident in all datasets. However, only at Keweenaw Point (U.S. EPA) was significant (p = 0.0005) declining trends in total PCB concentrations observed after 1995. In Wisconsin sites, the declining trend was marginal significant (p = 0.04) during the same period. In other locations, no temporal trends were found but large annual fluctuations occurred for unknown reasons. PCB concentrations at most sites have not achieved the reduction target of 100 ng/g ww for wildlife and human health protection established by the U.S. EPA. It is still difficult to predict when fish will be able to be consumed without limitation in this region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".