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Record W2886943487 · doi:10.24908/pceea.v0i0.10128

A Tool for Systematically Accessing the Level of Readiness of Engineering Design in Product Development

2018· article· en· W2886943487 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsOntario Tech UniversityUniversity of Toronto
Fundersnot available
KeywordsDeliverableNew product developmentSystems engineeringEngineering design processComputer scienceProduct designProduct (mathematics)Process (computing)Product design specificationManufacturing engineeringSoftware engineeringProcess managementEngineeringBusinessMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

Abstract – This paper introduces a top down, system-engineering approach to develop a quantifiable and systematic tool, referred to as Design Readiness Level (DRL), to gauge design at each stage of product development. It is developed to facilitate communication between different stockholders of a design project and to address the complexities arising during all the phases of product design, from initiation to completion. The design process as one of the pillars of DRL has been studied thoroughly and is categorized into nine stages to reflect the technical flow in product development. The design stages are iterative at any level from 1 to 9 and have distinctive deliverables at the end of each stage. The deliverables simplify and characterize the assessment of the design from the technical point of view. Developing a comprehensive DRL metrics that encompasses all the stockholders’ perspectives in a design is a work in progress.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.026
GPT teacher head0.238
Teacher spread0.212 · 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