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Record W2067960619 · doi:10.1080/0740817x.2010.540638

On the investment in a reliability improvement program for warranted second-hand items

2011· article· en· W2067960619 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

VenueIIE Transactions · 2011
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWarrantyReliability (semiconductor)UpgradeInvestment (military)Reliability engineeringProduct (mathematics)Action (physics)Computer scienceState (computer science)Return on investmentOperations researchRisk analysis (engineering)Actuarial scienceEngineeringOperations managementBusinessEconomicsMicroeconomicsProduction (economics)MathematicsPower (physics)

Abstract

fetched live from OpenAlex

A reliability improvement program (such as an upgrade action) can be seen as an investment by a dealer to restore a second-hand product to a better operational state. Due to the nature of the actions performed, the item's reliability at the end of this program is usually uncertain. This article develops a stochastic cost–benefit model for investment made in reliability improvement programs for second-hand items sold with failure-free warranty. Depending on the product's lifetime modeling approach, two modifications of the model are considered and are solved for the optimal improvement level. A real case application of the model is presented to validate the proposed approach.

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)0.000
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 score0.482
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.215
Teacher spread0.198 · 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