Cross-species comparison of relative potencies and relative sensitivities of fishes to dibenzo-<i>p</i>-dioxins, dibenzofurans, and polychlorinated biphenyls in vitro
Bibliographic record
Abstract
Dioxin-like compounds of varying toxicities are found in complex mixtures. The toxic equivalency factor (TEF) approach was developed based on the potency of a dioxin-like compound relative to the potency of 2,3,7,8-tetrachloro-dibenzo-p-dioxin (TCDD) to streamline risk assessment. One limitation of the TEF approach is uncertainty regarding differences in the relative potency of dioxin-like compounds among different species. Relative potencies among fishes are limited, relative to relative potencies among birds and mammals, and TEFs for fishes are based entirely on the model species, rainbow trout (Oncorhynchus mykiss). An in vitro liver explant assay was used to characterize species-specific responses with regard to up-regulation of CYP1A transcript after exposure to 6 dioxin-like compounds in rainbow trout, white sturgeon (Acipenser transmontanus), lake sturgeon (Acipenser fulvescens), and northern pike (Esox lucius). Differences in sensitivities were observed among species after exposure to dioxin-like compounds. The relative potencies developed from liver explants of rainbow trout were comparable to relative potencies developed from embryo toxicity assays. Differences in relative potencies between species with the least and greatest relative potencies were up to 40-fold. To compare relative potencies among species, concentrations of dioxin-like compounds in fish eggs in the Fraser River and in Lake Ontario were used to calculate toxic equivalency quotients (TEQs) determined from TEFs or TCDD equivalents determined from relative potencies. The TEQs underestimated TCDD equivalents for white sturgeon, lake sturgeon, and northern pike, indicating uncertainty in application of TEFs to diverse fishes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".