Deux Methodes de Recherche Locale pour Resoudre un Probleme d'Horaire du Personnel Infirmier dans un Etablissement Hospitalier
Bibliographic record
Abstract
Dans cet article, nous traitons le problemedeconfection d'horaire dupersonnel in rmier dans un etablissement hospitalier en tenant compte des contraintes issues de l'administration de l'h^opital et des desirs du personnel. Ce probleme est modelise comme un probleme de programmation mathematique multi-objectifs non-lineaires. Nous proposons pour sa resolution deux techniques de recherche locale. La premiere est une adaptation de la methode de recherche tabou qui a connu un grand succes pour resoudre plusieurs problemes d'optimisation combinatoire aunseulobjectif. La deuxieme technique est une adaptation des algorithmes genetiques. Ces deux heuristiques ont ete testees sur un jeu de donnees reelles issues de deux unites de soins de l'h^opital H^otel-Dieu de Montreal.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".