An approach to identify, prioritise and provide regulatory follow-up actions for new or emerging risks of chemicals for workers, consumers and the environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it