RISK ASSESSMENT AND USE OF RISK ACCEPTANCE CRITERIA FOR THE REGULATION OF DANGEROUS SUBSTANCES
Bibliographic record
Abstract
This paper gives an overview of the existing systems and arrangements for the regulation of establishments handling dangerous substances in seven different countries (six European countries, plus Canada). Focus is on risk assessment and type of risk acceptance criteria for the regulation of dangerous substances. The risk acceptance criteria may either be qualitative or quantitative; they may be deterministic or risk based. Further, risk often has to be balanced towards other factors, as cost and the importance of the activity to the society. Other topics of the paper are the identification and classification of hazardous installations, systems for land use planning and experiences gained from the current legislation. The arrangements in Canada differ from that of the European countries, and are given special attention. Finally, some general recommendations are given regarding the suitability and the use of risk acceptance criteria in the regulation of dangerous substances. 1.
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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 | 0.055 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".