Uncertainty analysis of dioxin-like polychlorinated biphenyls–related toxic equivalents in fish
Bibliographic record
Abstract
The toxic equivalent (TEQ) concept is widely used to assess toxicity potential of a dioxin-like chemical mixture. The TEQ approach converts concentrations of various dioxin-like compounds into a single concentration that is toxicologically equivalent to the most toxic dioxin compound, 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD), using toxic equivalency factors (TEFs). It has been shown that in the absence of costly measurements of dioxin-like polychlorinated biphenyls (dl-PCBs) in fish, relatively inexpensive measurements of total PCB can be utilized to estimate dl-PCB-related TEQ (i.e., TEQ(dl-PCB)). The present study assesses the impacts of uncertainties in dl-PCB measurements and estimates, and mammalian TEFs on TEQ(dl-PCB) using the Monte Carlo technique. The analysis suggests that measurement errors for dl-PCBs translate into up to 1.3-fold uncertainty in TEQ(dl-PCB), while uncertainties in estimates of dl-PCBs generally produce up to a threefold uncertainty in TEQ(dl-PCB). In contrast, the uncertainty due to TEFs normally ranges 10- to 13-fold and spans over 30- to 40-fold under extreme cases. For 2005 TEFs, PCB-126 is the dominating contributor to uncertainty in TEQ(dl-PCB). When we considered uncertainties in the TEFs and estimated dl-PCB concentrations simultaneously, there was little increase in uncertainty in TEQ(dl-PCB) that was already produced by the TEFs only. These results indicate that the dl-PCB composition in fish and/or the relationship between total PCB and TEQ(dl-PCB) can be utilized to estimate TEQ(dl-PCB) with reasonable confidence.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".