Refining adverse outcome pathways using Japanese Medaka Embryos (Oryzias latipes) exposed to 2,3,7,8-Tetrachlorodibenzodioxin
Bibliographic record
Abstract
Adverse outcome pathways (AOPs) are a framework that categorizes the impact of chemicals biologically from the initial molecular interaction through to the ecosystem level. This research aims to refine two existing AOPs that are initiated when dioxins and dioxin-like chemicals bind to molecular receptors. When embryonic development is adversely impacted by dioxins, we hypothesize that there are differences in gene and protein expression, which will distinguish the molecular level key events in these two AOPs. To test this, we performed qPCR on AOP key event genes, and also non-targeted proteomics on teleost embryos from different stages of development after exposure to dioxins and linked these to higher-level adverse effects, specifically cardiac impairment, and malformations. Refining these AOPs will benefit society by improving our ability to respond to chemical contaminants more effectively to prevent adverse outcomes in humans and their environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".