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Record W2793538941 · doi:10.3390/safety4010007

Failure Rates for Aging Aircraft

2018· article· en· W2793538941 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

VenueSafety · 2018
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFailure rateDegradation (telecommunications)Accelerated agingReliability engineeringAeronauticsForensic engineeringProcess (computing)Life spanEngineeringEnvironmental scienceComputer scienceGerontologyMedicineTelecommunications

Abstract

fetched live from OpenAlex

In any consideration of the operating condition, the age of a particular aircraft is a major factor. Much attention is focused on planes which are aged, that is, aircraft with chronological age or accumulated hours of use beyond a threshold. Being aged is a state, and should be distinguished from aging, which is a process of degradation with use. The degradation process starts at first flight and continues through time, with the rate being affected by aircraft design, patterns of use, and maintenance procedures. In this paper, the cycles, block hours and failures were recorded, and failure rates with accumulated use were calculated. A pattern of increasing failure rates with accumulated use (age) is observable, with improvement (decline in rate) at times of planned maintenance. The evidence supports the hypothesis that aging, that is increasing rates of failure, begins early in the life of an aircraft. Early evidence of degradation is also a precursor for accelerated failure rates as use accumulates along the age trajectory.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.205

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 head0.006
GPT teacher head0.235
Teacher spread0.229 · 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