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Record W3134325232 · doi:10.1117/12.2578919

High sensitivity transparent capacitive micromachined ultrasound transducer linear arrays for optical, photoacoustic and ultrasound imaging

2021· article· en· W3134325232 on OpenAlexaff
Afshin Kashani Ilkhechi, Christopher Ceroici, Roger J. Zemp

Bibliographic record

VenuePhotons Plus Ultrasound: Imaging and Sensing 2021 · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCapacitive micromachined ultrasonic transducersMaterials scienceTransducerUltrasoundIntravascular ultrasoundCapacitive sensingPhotoacoustic imaging in biomedicineUltrasonic sensorWaferAcousticsBiomedical engineeringOpticsOptoelectronicsComputer scienceRadiology

Abstract

fetched live from OpenAlex

We introduce transparent TOBE CMUT arrays for multimodal optical camera imaging, ultrasound imaging and photoacoustic imaging. We used an adhesive wafer bonding process technique to fabricate the 128-element lambda-pitch transducers and achieve high transparency ranging from visible to near-infrared with ultrasound center frequencies of 7.5-9 MHz. The arrays are anticipated to have utility for endoscopic, intravascular, and intra-operative probes for multimodal interventional guidance.

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 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 score0.467
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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
Published2021
Admission routes1
Has abstractyes

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