An approach to identify, prioritise and provide regulatory follow-up actions for new or emerging risks of chemicals for workers, consumers and the environment
Bibliographic record
Abstract
This paper illustrates a comprehensive and systematic approach for the identification of new or emerging risks of chemicals (NERCs) for workers, consumers and the environment. The methodology illustrated here is composed of three steps: 1) signal identification; 2) signal evaluation and prioritisation and when necessary; 3) assessing follow-up actions for further risk management measures. During signal identification, new information with regard to adverse effects induced by the potential NERC is gathered using various information sources. Based on collected additional information, the causality between chemical exposure and the adverse effect is evaluated and prioritised. Finally, for those NERCs where there is sufficient proof of the causality with an adverse effect or the need for action, an analysis of possible appropriate regulatory risk management options is made. With this approach, NERCs can be efficiently identified with timely recommendations of follow-up steps, to reduce or eliminate the risk of the substance.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".