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Record W2127018401 · doi:10.1109/med.2008.4602144

A multi-decision approach for decentralized diagnosis of the presence and absence of faults in discrete event systems

2008· article· en· W2127018401 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.

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

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPetri Nets in System Modeling
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedical diagnosisUndecidable problemComputer scienceFault (geology)Decision problemEvent (particle physics)TupleClass (philosophy)Decision support systemArtificial intelligenceMachine learningDistributed computingTheoretical computer scienceAlgorithmMathematicsDecidabilityMedicine

Abstract

fetched live from OpenAlex

We develop a multi-decision framework for decentralized diagnosis of discrete event systems (DES), where each diagnoser issues a tuple of diagnoses instead of a single diagnosis. We use the multi-decision framework to generalize existing methods for decentralized diagnosis. We study the diagnosis of the occurrence of fault as well as the diagnosis of the absence of fault. We show that the multi-decision permits to diagnose a broader class of systems. We also show how to tackle a potential undecidable problem that seems to arise with multi-decision.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.650
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.048
GPT teacher head0.292
Teacher spread0.244 · 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

Citations11
Published2008
Admission routes1
Has abstractyes

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