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Record W2126775929 · doi:10.1109/ivs.1995.528286

Intelligent maintenance support system for trucks

2002· article· en· W2126775929 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaInstituto Mexicano del Seguro SocialSyncrude
KeywordsComputer scienceExpert systemIntelligent decision support systemIntelligent sensorHypermediaInterpretation (philosophy)Condition monitoringTruckSystems engineeringSoftware engineeringArtificial intelligenceEngineeringWireless sensor networkWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

With the mechanical monitoring devices in place more accurate information may be derived, but as systems get more complex the interpretation of this information becomes more difficult, even impossible for the human expert. Computers can be used in the interpretation of these data, using numerical techniques in some cases or, more recently, expert systems. The expert system in condition monitoring, a relatively new concept, is an effective tool in interpreting large amounts of data. This paper describes a general intelligent system architecture for equipment monitoring and maintenance, known as the intelligent maintenance support system. It makes use of mechanical hardware monitoring systems, several intelligent analysis tools, and an intelligent hypermedia system that is implemented with INTEMOR.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.514

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 science0.0010.000
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 head0.049
GPT teacher head0.237
Teacher spread0.188 · 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