International horizon-style exercise (HSE): advancing the use of adverse outcome pathway (AOP) in radiation protection
Bibliographic record
Abstract
The Adverse Outcome Pathway (AOP) framework provides a means to integrate radiobiological and epidemiological data across different levels of biological organisation for an adverse outcome of interest to regulatory decision-making.The AOP approach is envisioned to improve understanding of radiation-induced effects at low doses and dose-rates and decrease the uncertainty in radiation health risk assessment.To explore the challenges in the use of AOPs, an international horizon-style exercise (HSE) was initiated through the Nuclear Energy Agency (NEA) High-Level Group on Low Dose Research (HLG-LDR) Radiation/Chemical (Rad/Chem) AOP joint topical group.The HSE was completed in three phases.First, candidate research questions were solicited from radiation risk professionals via a dedicated website.Second, the over 250 questions submitted were refined by a dedicated steering committee using a bestworst scaling method.During a virtual 3-day workshop, the list of questions was further refined to the top 25 priority questions.Lastly, an internet-based survey of the broader radiation risk community lead to an orderly ranking of the 25 priority questions, again using a best-worst scaling method.Major themes from the survey included the ability of AOPs to address different levels of biological organisation, radiation quality, dose or dose rate, time patterns, and confounding variables.Broadly, these efforts will help advance the use of AOP in radiation research and regulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".