Estimation des risques machines: Recensement des méthodes et subjectivité des paramètres de l'estimation
Bibliographic record
Abstract
Serious accidents are still caused by machinery-related risks in industry. One of the conditions for dealing with this problem is to design safe machines. At design stage, the first step of the prevention approach involves analysing all the risks likely to affect workers interacting with machinery, estimating and assessing these risks to develop a strategy aimed at reducing the risks. The process proposed by the relevant standards has been extensively described in the various guides issued by prevention organisations. Occupational health and safety institutes INRS and Robert Sauvé Research Institute (IRSST, Quebec) have considered a large-scale bibliographical study on risk assessment methods to be of significance. This study has underlined the predominance of matrix methods for determining risk levels, as well as a large variety in the parameters employed and in the terms which describe them. The study therefore reveals an adaptation of normative concepts. Another part of our work focuses on the difficulty for experts to integrate the values of the different parameters used in risk assessment. For instance, placing a risk exposure time of 6 hours on a 4-level discrete scale -2h/4h/8h/20h per week. We have applied probabilistic techniques for introducing the inaccuracy applicable to the values allocated to assessment input data. A software tool has been developed for facilitating risk ranking and provides a trend for the risk level. Furthermore, this tool ensures a gradual transition between each risk level.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".