MétaCan
Menu
Back to cohort
Record W2342229622 · doi:10.7863/jum.2001.20.8.891

An approach to acoustic properties of biological tissues using acoustic micrographs of attenuation constant and sound speed.

2001· article· en· W2342229622 on OpenAlexaff
Hiroaki Okawai, Kazuto Kobayashi, S. Nitta

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsAttenuationAcoustic attenuationSpeed of soundAcousticsDispersion (optics)Constant (computer programming)Acoustic dispersionAudio frequencyInterference (communication)Range (aeronautics)Sound powerAcoustic waveSound (geography)Biomedical engineeringMaterials scienceSound pressureMedicineComposite materialOpticsPhysics

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop a method for two-dimensional measurement of acoustic properties of biological tissue elements at the microscopic level for specimens with a thickness of approximately 10 microm. METHODS: The procedure was developed on the basis of mechanically scanned acoustic microscopic techniques in the frequency range of 100 to 200 MHz and the theory of interference phenomena. Various tissues and samples were prepared to evaluate the method and to interpret sound propagation properties. RESULTS: Tissues with high protein content, low water content, or both had a high attenuation constant and sound speed. The exponent n of attenuation against frequency was almost unity at the microscopic level, whereas it was greater than unity when the specimen thickness was greater. Sound speed dispersion was not observed. CONCLUSIONS: The method was shown to be reproducible, and the data were interpreted acoustically and pathologically with reference to tissue type and specimen thickness.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.052
GPT teacher head0.311
Teacher spread0.259 · 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 designObservational
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

Citations29
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Ultrasound in MedicineSame topicUltrasound Imaging and ElastographyFrench-language works237,207