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Record W4391997778 · doi:10.58286/29306

CIVA as an Aid to Understanding Ultrasonic Anisotropy in Steel

2024· article· en· W4391997778 on OpenAlexaff
Edward Ginzel

Bibliographic record

Venuee-Journal of Nondestructive Testing · 2024
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAnisotropyIsotropyMaterials scienceUltrasonic sensorCalibrationBeam (structure)AcousticsComposite materialShear (geology)Structural engineeringOpticsEngineeringPhysics

Abstract

fetched live from OpenAlex

Standard calibration blocks to assess beam paths and configure data displays using ultrasonic instruments use low carbon steel with isotropic acoustic properties. However, industry is trending towards the use of high tensile steels where the acoustic properties are anisotropic. When an ultrasonic system, calibrated on an isotropic steel reference block, is used on anisotropic steels, the beam can be bent, skewed and attenuated so that the reference setup is rendered useless. CIVA simulation software can be used to aid in understanding the effects on the data displays. As well, CIVA can help to demonstrate the degree of change that can occur when the acoustic velocities are established for the fast and slow shear modes in an anisotropic steel.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.006

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 head0.036
GPT teacher head0.267
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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