Prise en compte de l’état réel d'un système complexe pour l’aide à la décision en phase d'exploitation
Bibliographic record
Abstract
Cet article propose un modele d’aide au choix de sequences de conduite pour les systemes critiques. L’objectif ici consiste a proposer un classement multicritere de sequences en integrant a la fois l’aspect securitaire, dans la definition des criteres de decision, et egalement l’etat reel des equipements et les connaissances metiers. Une methodologie structuree pour supporter le processus de classement est ainsi developpee. Les modeles proposes reposent sur la theorie de l’utilite multi-attributs et la notion de bilan de sante. Les entrees de ces modeles proviennent des donnees reelles et des connaissances metiers. En sortie, l’expert de la conduite dispose d’un ensemble d’informations consensuelles et objectives sur le systeme. La principale originalite de ce travail consiste en la combinaison d’information issue du suivi « on line » du systeme et d’un outil issu de l’aide a la decision dans un objectif de pilotage de SED.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".