Age-Corrected Trends and Toxic Equivalence of PCDD/F and CP-PCBs in Lake Trout and Walleye from the Great Lakes: 2004–2014
Bibliographic record
Abstract
Our research reports polychlorinated dibenzo- p -dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and coplanar polychlorinated biphenyls (CP-PCBs) concentrations and age-corrected trends for lake trout and walleye in the Great Lakes over the 2004–2014 period. We determined that age–contaminant corrections are required to accurately report contaminant trends due to significant lake trout age structure changes. The age-trend model (ATM) described here uses a lake-specific age–contaminant regression to mitigate the effect of a fluctuating lake trout age structure to directly improve the log–linear regression model. ATM results indicate that half-life ( t 1/2 ) and percent decreases for PCDD/Fs, CP-PCBs, and toxic equivalence (TEQ) (average −56 to 70%) were fairly uniform and consistent across the Great Lakes over the 2004–2014 period. The vast majority of TEQ associated with all Great Lakes lake trout and walleye samples is due to the nonortho CP-PCBs (average = 79%) as compared with PCDD/Fs (average = 21%). On average, CP-PCB_126 individually accounted for over 95% of the total CP-PCB TEQ. A retrospective analysis (1977–2014) of 2378-TCDF and 2378-TCDD raw concentrations in Lake Ontario lake trout revealed decreases of 94% and 96%, respectively. Tissue residue guidelines for wildlife protection based on lake trout and walleye total TEQ were uniformly exceeded in all the Great Lakes.
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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 | 0.000 | 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".