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Record W4232362682 · doi:10.32920/ryerson.14639199.v1

Defining the Engine Design Process

2021· preprint· en· W4232362682 on OpenAlexaff
Filippo A. Salustri

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsInformation flowProcess (computing)Component (thermodynamics)Design processComputer scienceQuality (philosophy)Product (mathematics)Product designProcess designEngineering design processSystems engineeringEngineeringProcess managementOperations managementWork in process

Abstract

fetched live from OpenAlex

Shortening design lead times while maintaining product quality requires careful planning of the design process. This paper describes a technique used at a major automobile manufacturer to create a simple, flexible process to do just that for their engine design group. The design organization's communications infrastructure was tailored to assure that design information was reliably transmitted through the organization. The mechanism used to achieve this accounts for (a) both component and system perspectives, and (b) the unpredictable nature of the flow of design information by treating unpredictability as an acceptable, even desirable, state of affairs.

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 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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.466
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.286
Teacher spread0.261 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

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