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Record W2045401621 · doi:10.1243/0954405011519231

Calculation of product architecture metrics within a solid modeller

2001· article· en· W2045401621 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

VenueProceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsComputer scienceModular designArchitectureProduct (mathematics)Measure (data warehouse)Function (biology)MODELLERData miningMathematicsProgramming language

Abstract

fetched live from OpenAlex

The architecture of a product is defined as the scheme in which functions are mapped to physical components. Architecture affects how a product satisfies design objectives. There are several ways to measure architecture, and one is implemented into a solid modelling program that will do the quantification automatically. The software used is the I-DEAS solid modelling package, for which an internal program file was created to perform the calculations automatically. This program works by counting the parts that the user has created and then uses an internal I-DEAS function to find all of the joined parts. The program counts the joints and then prompts the user for the strength of each joint. With this information, an adjusted parts connectivity and average joint strength are calculated and can be used to evaluate the degree to which the architecture of a product is either integral or modular. Several case studies are presented that were used to evaluate the effectiveness of the program.

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)0.001
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 score0.714
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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