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Record W2321117128 · doi:10.1063/1.4940541

In-situ calibration of pulsed eddy current detection of cracks at fasteners in CP-140 aircraft

2016· article· en· W2321117128 on OpenAlexaff
Ross Underhill, C. Stott, Thomas W. Krause

Bibliographic record

VenueAIP conference proceedings · 2016
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsMahalanobis distanceCalibrationEddy-current testingRange (aeronautics)Eddy currentStructural engineeringComputer scienceAcousticsMaterials scienceEngineeringArtificial intelligenceMathematicsComposite materialPhysicsStatisticsElectrical engineering

Abstract

fetched live from OpenAlex

The use of the Smallest Half Volume (SHV) robust statistics method and the Mahalanobis distance to blindly distinguish fasteners with cracks from fasteners without is examined. Pulsed eddy current data obtained from CP140 Aurora wing structures is used to test the approach. It is shown that the method can achieve levels of detection very close to those obtained when the same measurement technique is applied with full knowledge of which fasteners have no cracks. The method is applicable to a broad range of similar situations when an objective hit/miss criterion is used.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.567

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 communication0.0000.001
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 head0.245
Teacher spread0.227 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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