MétaCan
Menu
Back to cohort
Record W2024358447 · doi:10.1115/1.4030077

Task-Oriented Adaptive Maintenance Support System

2015· article· en· W2024358447 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

VenueJournal of Computing and Information Science in Engineering · 2015
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsTask (project management)Computer scienceProcess (computing)DocumentationSoftware engineeringSoftwareTechnicianTechnical documentationHuman–computer interactionSystems engineeringEngineering

Abstract

fetched live from OpenAlex

Technical manuals for complex systems like automobiles, airplanes, and machine tools, often consist of a huge amount of documentation containing disassembling, assembling instructions and drawings of parts, subassemblies, and exploded views. So it is difficult for users to find a piece of information they need amongst these huge amount of documentations. In order to support maintenance implementation process effectively, a task-oriented adaptive maintenance support (TOAMS) system is designed to provide an intelligent, adaptive electronic support for maintaining complex equipment according to user profiles and their work in hand. By building user model, task model, and product model, document model is configured by multiview and some pieces of semantic information are added into data modules. An agent based software application is being developed to support and allow a systematic utilization of the “adaptive response” by integrating it into the daily work of the technician. An application is presented to show the applicability of our method.

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)0.002
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 score0.729
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.005
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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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