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Record W2124986335 · doi:10.1109/rams.2007.328113

Quadruple "A" Model: Reliability Growth Over Product Generations

2007· article· en· W2124986335 on OpenAlexaff
P.K. Au, Stephen K. Chiu, Darryl Bacheldor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsFlex (Canada)
Fundersnot available
KeywordsReliability (semiconductor)Product (mathematics)Product lifecycleComputer scienceProduct life-cycle managementPlan (archaeology)New product developmentQuality (philosophy)Reliability engineeringProcess managementOperations researchEngineeringBusinessMarketing

Abstract

fetched live from OpenAlex

A strategic life cycle model, coined Triple "A", was developed in 2000 at a major telecommunications manufacturer to accelerate the reliability growth of new products by allocating resources in alignment with the characteristics and demand of different product phases. The implementation tactics, the estimated financial results achieved in a pilot product X, and lessons learned are documented in the paper, "Lessons Learned From Development And Implementation Of A Strategic Life Cycle Model," presented at RAMS 2004. A hypothesis was developed in the 2004 paper in terms of the life cycle quality to be achieved as the result of implementation. The present paper is to validate this hypothesis through real-life data collected over the past several years, and then to apply the result to next generation products. Finally, lessons learned and a going-forward plan is tabled.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.002
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 head0.007
GPT teacher head0.232
Teacher spread0.224 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2007
Admission routes1
Has abstractyes

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