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Record W1547533744 · doi:10.1017/s0001924000010150

Bayesian sensitivity analysis of flight parameters that affect main landing gear yield locations

2014· article· en· W1547533744 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

VenueThe Aeronautical Journal · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsSafran Electronics (Canada)
FundersEngineering and Physical Sciences Research Council
KeywordsLanding gearDescent (aeronautics)Sensitivity (control systems)Structural engineeringBending momentShock (circulatory)EngineeringControl theory (sociology)Aerospace engineeringComputer science

Abstract

fetched live from OpenAlex

Abstract An aircraft and landing gear loads model was developed to assess the Margin of Safety ( MS ) in main landing gear components such as the main fitting, sliding tube and shock absorber upper diaphragm tube. Using a technique of Bayesian sensitivity analysis, a number of flight parameters were varied in the aircraft and landing gear loads model to gain an understanding of the sensitivity of the MS of the main landing gear components to the individual flight parameters in symmetric two-point landings. The significant flight parameters to the main fitting MS , sliding tube bending moment MS and shock absorber upper diaphragm tube MS include: longitudinal tyre-runway friction coefficient, aircraft vertical descent velocity, aircraft Euler pitch angle and aircraft mass. It was also shown that shock absorber servicing state and tyre pressure do not contribute significantly to the MS .

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)0.009
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.885
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.095
GPT teacher head0.324
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