Estimating dioxin-like polychlorinated biphenyl toxic equivalents from total polychlorinated biphenyl measurements in fish
Bibliographic record
Abstract
Polychlorinated biphenyls (PCBs) are 209 related compounds, a dozen of which are known as dioxin-like PCBs (dl-PCBs) and are among the most toxic PCBs. Polychlorinated biphenyls contribute to many adverse effects to human health, including cancer, and are a major cause of fish advisories in North America. It is a common perception that individual PCB compounds, especially dl-PCBs, rather than total PCB need to be quantified to predict the environmental hazard because of differences in their toxicity potential and distribution among various environmental matrices, including aquatic food webs. Because the current analytical methods for quantifying dl-PCBs are complex and four- to fivefold more expensive, limited fish samples are analyzed for dl-PCBs. Using what likely is the largest dl-PCB fish data set (n = 912) with a wide distribution of fish species (n = 22), size (19-112 cm), weight (100-14,300 g), sex (male:female, 51:49), and PCB contamination level (20-7,300 ng/g wet wt), we show that the comparatively less expensive and rapid measurements of total PCB in fish can be utilized to assess dl-PCB-related toxicological hazard, measured as 2,3,7,8-tetrachlorodibenzo-p-dioxin toxic equivalents (TEQ). A regression equation of dl-PCB-related TEQ (i.e., TEQdl-PCB) to total PCB in fish is presented (TEQdi-PCB = [2.56 x 10(-5)]CtotalPCB, r = 0.89, p < 0.001). The regression was evaluated by applying it to three independent data sets of substantial sizes (n = 55, 141, and 176). The TEQdl-PCB estimated using the regression and total PCB measurements were within a reasonable factor of two to three of the TEQdl-PCB calculated from the dl-PCB measurements. The successful evaluation indicates versatility of the regression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
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".