Reevaluation of 2,3,7,8-Tetrachlorodibenzo-<i>p</i>-Dioxin Equivalency Factors for Dioxin-Like Polychlorinated Dibenzo-<i>p</i>-Dioxins, Polychlorinated Dibenzofurans, and Polychlorinated Biphenyls for Fishes
Bibliographic record
Abstract
An expert meeting was organized by the World Health Organization (WHO) in 1997 to streamline assessments of risk posed by mixtures of dioxin-like chemicals (DLCs) through development of 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) equivalency factors (TEFs) for mammals, birds, and fishes. No reevaluation has been performed for fish TEFs. Therefore, the objective of the present study was to reevaluate the TEFs for fishes based on an updated database of relative potencies (RePs) for DLCs. Selection criteria consistent with the WHO meeting resulted in 53 RePs across 14 species of fish ultimately being considered. Of these RePs, 70% were not available at the time of the WHO meeting. These RePs were used to develop updated TEFs for fishes based on a similar decision process as used at the WHO meeting. The updated TEF for 16 DLCs was greater than the WHO TEF, but only four differed by more than an order of magnitude. Measured concentrations of DLCs in four environmental samples were used to compare 2,3,7,8-TCDD equivalents (TEQs) calculated using the WHO TEFs relative to the updated TEFs. The TEQs for none of these environmental samples differed by more than an order of magnitude. Therefore, present knowledge supports that the WHO TEFs are suitable potency estimates for fishes. However, the updated TEFs pull from a larger database with a greater breadth of data and as a result offer greater confidence relative to the WHO TEFs. Risk assessors will have different criteria in the selection of TEFs, and the updated TEFs are not meant to immediately replace the formal WHO TEFs; but those who value a larger database and increased confidence in TEQs could consider using the updated TEFs. Environ Toxicol Chem 2023;42:2215-2228. © 2023 Wiley Periodicals LLC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".