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Record W1988575859 · doi:10.2514/2.2750

Risk Analysis of Fuselage Splices Containing Multisite Damage and Corrosion

2001· article· en· W1988575859 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

VenueJournal of Aircraft · 2001
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsNational Research Council Canada
FundersSouthwest Research InstituteBoeing
KeywordsFuselageCorrosionStructural engineeringMaterials scienceAerospace engineeringEngineeringForensic engineeringComposite material

Abstract

fetched live from OpenAlex

repair of the components. This technology has been increasingly used in the maintenance and management of aging aircraft e eets to improve e ight safety and reduce costs. The output of risk analysis can be used to optimize the maintenance schedule of aging aircraft while maintaining the POF under an acceptable level. To examine the performance of fatigue critical structures, probabilistic risk analysis has been applied to both military and commercial aircraft, and it is believed to be the approach of choice for the future. 1 Various probabilistic risk analysis methodologies have been developed for aging aircraft structures. Generally, there are two aspects in the probabilistic risk analysis to be noted. First is deterministic damage tolerance and durability analysis, such as the analysis of the median crack growth data, the onset pattern of initial MSD, crack linkup criterion, and failure criterion. Considerable effort has been expended on this issue using fracture

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.042
GPT teacher head0.323
Teacher spread0.281 · 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