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Record W4391972568 · doi:10.3233/jid-230070

Environment-driven evolution analysis of a product: A case study of braking system evolution

2024· article· en· W4391972568 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

VenueJournal of Integrated Design and Process Science · 2024
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsConcordia University
Fundersnot available
KeywordsProduct (mathematics)Computer scienceProduct designNew product developmentSystems engineeringData scienceProcess managementEngineeringBusinessMarketing

Abstract

fetched live from OpenAlex

In response to evolving societal and technical demands, this research explores the dynamic landscape of product evolution, focusing on the case study of braking systems. Acknowledging the critical role of product evolution analysis in design phases, the study introduces the Environment-Based Design (EBD) methodology. EBD emphasizes environmental analysis before delving into product specifics, employing tools like Recursive Object Model (ROM) diagrams and questioning-and-answering analyses. The paper systematically unfolds with a literature review highlighting various design methodologies, followed by the EBD application in a braking system evolution analysis. Trends in environment components are dissected, emphasizing the increasing influence of the human environment. The discussion underlines the significance of analyzing environment components in product evolution and asserts EBD’s applicability. Despite limitations, such as the exclusive focus on braking systems, the study contributes to understanding product evolution dynamics and advocates for the continued exploration of EBD across diverse products and cultural contexts.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.021
GPT teacher head0.284
Teacher spread0.263 · 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