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Record W3207631969 · doi:10.3389/frym.2021.618465

Examining Blood Vessels Using Sound Waves

2021· article· en· W3207631969 on OpenAlexaff
Christine M. Tallon, Kurt J. Smith, N. Lewis, Ali M. McManus

Bibliographic record

VenueFrontiers for Young Minds · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsSound (geography)Blood flowBlood vesselWork (physics)MedicineAcousticsCardiologyEngineeringInternal medicineMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Your heart works as a team with your body’s blood vessels. Your heart pumps the blood, while blood vessels help the blood travel all over your body, just as a garden hose helps move water all around your garden. Blood carries oxygen and other nutrients to your muscles and organs and removes carbon dioxide and other wastes. These are very important chores for the blood! Sometimes doctors need to check whether the heart and blood vessels are working properly, or scientists may want to study how blood vessels work as people get older. To do this, a technique called ultrasound is used to take pictures and videos of the blood vessels and the blood moving through them. But how does sound create pictures? This article will explain how ultrasound works and how it can be used to examine blood vessels and the speed of the blood flow.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.038
GPT teacher head0.294
Teacher spread0.256 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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