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Record W7100191282

Deux Methodes de Recherche Locale pour Resoudre un Probleme d'Horaire du Personnel Infirmier dans un Etablissement Hospitalier

2010· article· en· W7100191282 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicScheduling and Timetabling Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptation (eye)Raising (metalworking)Context (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.520
GPT teacher head0.498
Teacher spread0.022 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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