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Record W2166779075 · doi:10.1177/1063293x02010002638

HOME: House Of Modular Enhancement—a Tool for Modular Product Redesign

2002· article· en· W2166779075 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

VenueConcurrent Engineering · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsUniversity of SaskatchewanUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModular designModular programmingSystems engineeringProduct engineeringManufacturing engineeringProduct design specificationProduct (mathematics)Mass customizationProduct designEngineeringProduct lifecycleControl reconfigurationComputer scienceSoftwarePersonalizationSoftware engineeringNew product developmentEmbedded systemBusiness

Abstract

fetched live from OpenAlex

Product modularization aims to improve the overall design, manufacturing, operational, and post-retirement characteristics of products by designing or redesigning the product architectures. A successful modular product can assist the reconfiguration of products, while reducing the lead-time of design and manufacturing and improving the ability for upgrading, maintenance, customization and recycling. This paper presents a new modular design method called the House Of Modular Enhancement (HOME) for product redesign. Information from various aspects of the product design, including functional requirements, product architecture and life cycle requirements, is incorporated in the method to help ensure that a modularized product would achieve the objectives. The HOME method has been implemented in a software system. A case study will be presented to illustrate the HOME method and the software.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.871

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 communication0.0000.001
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 head0.018
GPT teacher head0.189
Teacher spread0.171 · 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