Hydroxylated PCBs and Other Chlorinated Phenolic Compounds in Lake Trout (<i>Salvelinus namaycush</i>) Blood Plasma from the Great Lakes Region
Bibliographic record
Abstract
Recently, there has been an increase in studies focusing on an emerging class of organic contaminants, hydroxylated PCBs (OH-PCBs) and chlorinated phenolic compounds (CPCs) in the environment, particularly in northern regions of Europe and Canada. Since information for fish from the Great Lakes are scarce, we determined the blood plasma concentrations of OH-PCB congeners, pentachlorophenol (PCP), 2,3,4,5-tetrachlorophenol (TCP), and 4-hydroxyheptachlorostyrene (4-OH-HpCS) for lake trout (Savelinus namaycush) collected from two of the Great Lakes, Lake Ontario and Lake Superior, and two regional lakes, Lake Champlain and Lake Opeongo. PCP was the dominant CPC in lake trout (105-658 pg/g of plasma). Detectable concentrations of 2,3,4,5-TCP and 4-OH-HpCS were found in all lake trout (2.6-101 and 0.4-27 pg/g, respectively). Highest concentrations were found in trout from Lake Ontario and Lake Superior. Sixteen OH-PCBs were quantified, with 4-OH-CB187 having the highest concentration in all samples (10-173 pg/g of plasma). Unexpectedly, highly chlorinated OH-PCBs such as 4'-OH-CB199 (mean 21.4 and 74.4 pg/g), 4,4'-diOH-CB202 (18.3 and 27.7 pg/g), and 4'-OH-CB208 (24.5 and 34.7 pg/g) were found in lake trout from Lake Ontario and Lake Superior, respectively. Future studies to delineate the sources and impacts of CPCs in the Great Lakes catchment are needed.
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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) | 0.001 | 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".