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Record W2769903961 · doi:10.1109/ultsym.2017.8091573

Microfluidic shrinking of microbubble contrast agents

2017· article· en· W2769903961 on OpenAlexaff
Vaskar Gnyawali, Byeong‐Ui Moon, Jennifer Kieda, Raffi Karshafian, Michael C. Kolios, Scott Tsai

Bibliographic record

Venue2017 IEEE International Ultrasonics Symposium (IUS) · 2017
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicrobubblesMaterials scienceMicrofluidicsUltrasoundContrast (vision)NanotechnologyBiomedical engineeringAcousticsComputer scienceMedicinePhysics

Abstract

fetched live from OpenAlex

Lipid-stabilized microbubbles are used in various clinical ultrasound (US) applications such as diagnostics imaging and therapeutics. The characteristics of the microbubbles such as size, shell material, and US frequency dictate the acoustic response of these microbubbles. Therefore, monodispersed microbubbles with the length-scale (1-7 μm in diameter) relevant to the biomedical ultrasound applications are highly desirable. However, generating monodispersed microbubbles with sub-micron size control has been challenging.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.024
GPT teacher head0.269
Teacher spread0.245 · 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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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