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Record W1970050048 · doi:10.1121/1.2884083

Tissue mimicking materials for dental ultrasound

2008· letter· en· W1970050048 on OpenAlexfundno aff
Rahul Singh, Martin O. Culjat, Warren S. Grundfest, E. R. Brown, Shane N. White

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2008
Typeletter
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsnot available
FundersNational Institute of Dental and Craniofacial ResearchNational Institutes of HealthMcMaster UniversityTelemedicine and Advanced Technology Research CenterU.S. Department of Defense
KeywordsMaterials scienceDentinImaging phantomUltrasoundHard tissueAttenuationBiomedical engineeringAcoustic impedanceSoft tissueEnamel paintAcousticsDentistryComposite materialOpticsMedicineRadiologyUltrasonic sensor

Abstract

fetched live from OpenAlex

While acoustic tissue mimicking materials have been explored for a variety of soft and hard biological tissues, no dental hard tissue mimicking materials have been characterized. Tooth phantoms are necessary to better understand acoustic phenomenology within the tooth environment and to accelerate the advancement of dental ultrasound imaging systems. In this study, soda lime glass and dental composite were explored as surrogates for human enamel and dentin, respectively, in terms of compressional velocity, attenuation, and acoustic impedance. The results suggest that a tooth phantom consisting of glass and composite can effectively mimic the acoustic behavior of a natural human tooth.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0050.003

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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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 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

Citations16
Published2008
Admission routes1
Has abstractyes

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