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Record W3116082497 · doi:10.1115/detc2000/dtm-14561

Computer Based Requirement and Concept Modelling: Information Gathering and Classification

2000· article· en· W3116082497 on OpenAlexaboutno aff
Fredrik Andersson, Patrik Nilsson, Hans Johannesson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceFunctional requirementFunctional decompositionDecompositionRepresentation (politics)Systems engineeringFunction (biology)Conceptual designSoftwareObject (grammar)Software engineeringTruckRequirements analysisObject-oriented designObject-oriented programmingHuman–computer interactionProgramming languageArtificial intelligenceEngineeringMachine learning

Abstract

fetched live from OpenAlex

Abstract This paper proposes a requirement and concept model based on a functional decomposition of mechanical systems. It is an object-oriented approach to integrate the representation of the design artefact and the design activity, through the decisions made during the design evolution. The requirements co-evolve simultaneously with the formation of the conceptual layout, through the opportunity to alter between function and physical/abstract solutions. This approach structures the design requirements and concepts in such a way that it supports the ability to document their sources, to allow for validation and verifications of both requirements and design solutions. First, the proposed model is presented from a theoretical viewpoint. Secondly, a methodology for modelling requirements and concepts in an object-oriented fashion is discussed. Finally, the model is implemented in METIS software and tested in a case study of an electric window winder on a truck door.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.017
GPT teacher head0.196
Teacher spread0.179 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2000
Admission routes1
Has abstractyes

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