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Record W2111537628 · doi:10.1109/pacrim.1991.160755

An intelligent basis for design

2002· article· en· W2111537628 on OpenAlexafffund
Thomas Calvert, John Dickinson, John C. Dill, W.S. Havens, J. D. Jones, Lyn Bartram

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicConstraint Satisfaction and Optimization
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCADComputer scienceExpert systemBacktrackingConstraint (computer-aided design)Knowledge baseProcess (computing)Dependency (UML)Software engineeringObject (grammar)Human–computer interactionComputer Aided DesignSystems engineeringEngineering drawingArtificial intelligenceEngineeringProgramming languageOperating system

Abstract

fetched live from OpenAlex

A description is presented of the first steps towards an intelligent CAD system based on a mixed-initiative approach. The expert system is an object-oriented model-based reasoning engine using constraint propagation and dependency backtracking. The architecture is built around the expert system, a CAD engine, and a user interface/controller module mediating the mixed-initiative relationship between designer and system. Initial experience has shown the need to model both experts and objects in the knowledge base and both the need and difficulty of representing design and constraint spaces to the designer. The preliminary results of a study of how people use a CAD system to better understand the design process itself suggest directions for the development of intelligent CAD.>

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)0.003
metaresearch head score (Gemma)0.004
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 score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.011
Scholarly communication0.0080.007
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.007

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 head0.066
GPT teacher head0.268
Teacher spread0.202 · 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

Citations3
Published2002
Admission routes2
Has abstractyes

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