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Record W2146379040 · doi:10.1109/iembs.1990.691082

Common Misconceptions About The Scattering Of Ultrasound By Blood

2005· article· en· W2146379040 on OpenAlexaff
L.Y.L. Mo, R.S.C. Cobold, K. Kirk Shung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsScatteringHematocritUltrasoundWaveletBackscatter (email)Variance (accounting)Computational physicsPhysicsMaterials scienceOpticsStatisticsMathematicsAcousticsComputer scienceMedicineTelecommunicationsArtificial intelligenceInternal medicineAccounting

Abstract

fetched live from OpenAlex

A new unifying approach for modelling the scattering of ultrasound in blood is described. The approach consists of summing the backscattered wavelets from elemental acoustic volumes each being occupied by a large number of red cells. It is shown that the average backscattered power is in general proportional to the variance rather than to the mean, of the local red cell concentration. Consequently, some common misconceptions regarding the relationship between the backscattering coefficient and hematocrit can be readily explained.

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)0.026
metaresearch head score (Gemma)0.047
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 score0.026
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.002
Science and technology studies0.0030.045
Scholarly communication0.0050.012
Open science0.0060.004
Research integrity0.0060.019
Insufficient payload (model declined to judge)0.0020.002

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 head0.010
GPT teacher head0.259
Teacher spread0.249 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

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