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Record W4200334136 · doi:10.53102/2021.35.01.858

Diagnostic 4.0 du système physique de production : modèle de référence d’audit des gammes de production

2021· article· fr· W4200334136 on OpenAlexaff
David Damand, Marc Barth, Abdellatif Dkhil

Bibliographic record

VenueRevue Française de Gestion Industrielle · 2021
Typearticle
Languagefr
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsHumanitiesProduction (economics)MathematicsArtEconomics

Abstract

fetched live from OpenAlex

L’industrie 4.0 est le résultat de la convergence entre l’industrie et le numérique. L’industrie 4.0 repose sur une relation homme-machine plus collaborative. Cette collaboration de type réseau est caractérisée par une communication continue et instantanée entre les moyens de productions et d’approvisionnement. Ce qui permet une surveillance continue d’éventuelles dérives de performance du système physique de production. Cependant, la qualité de cette nouvelle organisation en réseau des moyens de production dépend de la qualité des conditions initiales du système physique de production. Pour contribuer au diagnostic du système physique de production, ce papier propose un modèle de référence d’audit des gammes de production.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.207
Teacher spread0.179 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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