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Record W4206891296 · doi:10.1002/iis2.12875

Development of a digital twin for collaborative decision‐making, based on a multi‐agent system: application to prescriptive maintenance

2022· article· en· W4206891296 on OpenAlex
Quentin Lorente, Éric Villeneuve, Christophe Merlo, Guy André Boy, François Thermy

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueINCOSE International Symposium · 2022
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsSafran Electronics (Canada)
FundersAssociation Nationale de la Recherche et de la Technologie
KeywordsComputer scienceHuman–computer interactionProcess managementKnowledge managementEngineering

Abstract

fetched live from OpenAlex

Abstract The fourth industrial revolution involves more complexity. This research effort focuses on decision‐making in helicopter engine maintenance activities. Such a decision‐making task is difficult and relies on a variety of experts who only have partial knowledge and incomplete situation awareness, due to the great diversity of everyday operational practices. In this paper, we propose a digital twin multi‐agent approach to collaborative decision‐making in prescriptive maintenance.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.255
Teacher spread0.240 · 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