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Record W2037468332 · doi:10.1115/detc2009-87590

Guiding Engineering Design Activities Through a Question Based Approach

2009· article· en· W2037468332 on OpenAlexaff
Khadidja Grebici, Marco Aurisicchio, Rob Bracewell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsMcGill University
Fundersnot available
KeywordsAerospaceComputer scienceTask (project management)Exploratory researchDesign knowledgeSystems engineeringSoftware engineeringEngineering managementKnowledge managementEngineering

Abstract

fetched live from OpenAlex

The design of complex systems requires accurate planning and sequencing of the processes into task and activities. Companies such as the aerospace engine manufacturers demand effective methods and tools to support engineering designers in their tasks and activities. This paper presents exploratory research to investigate the possibility of supporting design activities through a question based approach. Sets of generic questions are extracted from the analysis of the question types proposed in previous research. The paper proposes that designers could be guided into their design inquiries by consulting the generic questions identified in this research and design knowledge indexes.

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)0.036
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.036
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.053
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.004
Science and technology studies0.0020.005
Scholarly communication0.0080.010
Open science0.0050.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.055
GPT teacher head0.264
Teacher spread0.209 · 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 designNot applicable
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
Published2009
Admission routes1
Has abstractyes

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