Méthodologie d'analyse des dysfonctionnements des systèmes pour une meilleure maitrise des risques industriels dans les PME : application au secteur du traitement de surface
Bibliographic record
Abstract
According to the general principles of prevention such as they are defined by the legislative code concerning the workers protection, a safety risks analysis must be done in all the industrial facilities. In small and medium-sized firms, this principle of prevention is rarely respected and we may think that it is partly because the methods of risks analysis which are proposed to these firms are not well adapted. In order to measure the level of adaptability or inadaptability of the existings methods, the characteristics of these small and medium-sized firms were studied, then the decision was taken to test a method of risks analysis in real conditions so as to study the obstacles which could appear in this situation. A classical method of risks analysis (MOSAR) was used in three small and medium-sized metal finishing firms. This method was chosen among several methods from the system safety movement. During this experimental work, numerous obstacles appeared at each step of the analysis i.e. : the modelisation of the system, the identification of the various possibilities of accidents, the definition of a hierarchy and choice of protection and prevention measures. These obstacles were studied and were found to be the result of a difference of values between the small and medium-sized firms and the prevention institutions and of a lack of model for the occurrence and representation of risk in these firms, as, in the institutions. Eventually various improvements were proposed that could help increasing the application of successful risk analysis in small and medium sized firms.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".