Apport du retour d'expérience à la maîtrise des risques relatifs à l'hygiène, la sécurité et l'environnement, dans les petits établissements industriels : application à l'industrie du traitement thermique
Bibliographic record
Abstract
It is admitted that small plants with the same features that Small and Medium-sized Enterprises (SME) often do not properly manage risks that may affect their staff and their human, natural and material environment. They seem to be late with regard to large companies whose risk management has been improving for the last decades. The development and implementation of concepts, methods and tools has contributed to a better awareness from industrials and improved decision-making in term of industrial risks. Accident/incident reporting has participated to this improvement dynamics. The research question of this thesis is about the capacity to apply accident/incident reporting within small plants. The concept of accident/incident reporting appears to be relevant for them. However, reporting systems that have been developed and used by large firms are not adapted to small plants with SMEs' features. An intermediate level between concept and accident reporting systems will be studied in order to transfer the lessons learned by large firms to them. This level is described as a set of characteristics which allows to define an accident/incident reporting system. These characteristics concern the processes of data reporting and analysis, the type of experience that is considered and the type of organisation which implements accident/incident reporting. It appears that the first need of small plants is to be helped to assess their risks. Thus a risk assessment tool was designed and developed for a group of industrial firms. This tool integrates data from accident reporting to an a priori risk assessment method. In this context, the conditions of implementation of accident reporting within small plants appear to be the necessity that several plants share the accident reporting system and that the accident reporting is integrated within an operational tool. These accident reporting system helps risk assessment : it provides the user with many data that he may have difficulties to obtain. Accident reporting allows the creation of a reference which may benefit SMEs unsuited for the development of the tool.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".