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Record W2088202845 · doi:10.1063/1.1287041

Ultrasonic imaging, particle detection, and V(z) measurements in molten zinc using focused clad buffer rods

2000· article· en· W2088202845 on OpenAlexaff
Ikuo IHARA, C. K. Jen, Demartonne Ramos França

Bibliographic record

VenueReview of Scientific Instruments · 2000
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsMcGill UniversityNational Research Council Canada
FundersNagaoka University of Technology
KeywordsMaterials scienceUltrasonic sensorRodOpticsLens (geology)ZincFocal lengthParticle (ecology)CeramicAcousticsComposite materialPhysics

Abstract

fetched live from OpenAlex

Ultrasonic imaging, particle detection, and V(z) measurements have been performed using focused clad buffer rods in molten zinc at temperature more than 600 °C. The focused ultrasonic waves are generated by a spherical or cylindrical acoustic lens which is fabricated at the end of the clad buffer rod. In order to evaluate its focusing ability, several experiments are carried out at 10 MHz in a pulse-echo mode. The lateral resolution at the focus of the spherical acoustic lens in molten zinc is quantitatively examined and compared with that in water using a thin stainless wire with a diameter of 380 μm. High resolution ultrasonic imaging is carried out by the common C-scan technique. The signal-to-noise ratio of the reflected signals from the flat sample surface at the focus is better than 35 dB. Ultrasonic images are obtained from the amplitude and time delay variations of the reflected signals. An attempt has also been made to detect particles suspended in molten zinc. Backscattered signals from particles are clearly visible at the focal region of the lens. For quantitative materials evaluation, V(z) curve measurements are performed using both spherical and cylindrical surface lenses and the leaky surface acoustic wave velocity of a ceramic (SiC) plate immersed in molten zinc is successfully determined.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.241
Teacher spread0.220 · 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 designOther design
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

Citations24
Published2000
Admission routes1
Has abstractyes

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