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Record W2994744449 · doi:10.1109/ultsym.2019.8925672

Differential frequency-domain photoacoustic microscope for blood oxygen saturation measurements

2019· article· en· W2994744449 on OpenAlexaff
Krishnan Sathiyamoorthy, Michael C. Kolios

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicroscopeMaterials scienceLaserChopperOpticsOptoelectronicsVoltagePhysics

Abstract

fetched live from OpenAlex

We have developed a low-cost frequency domain differential photoacoustic microscope. The microscope uses a low-cost PA sensor made up of a kHz microphone, low power CW lasers and an optical chopper with 6/5 dual-slot disc. The system used dual-slot chopper disc to modulate two laser beams simultaneously and the corresponding PA signals were measured. This simultaneous modulation enabled a single scan instead of two scans required for two lasers in previous configurations, reducing the scanning time half. The configuration also enabled two PA interrogations at the same location of the sample whereas in the conventional sequential measuring system any backslash of the translation stage would alter the location of the interrogation area during the next measurement. The developed sensor is used to study the oxygen saturation of a single RBC. Two types of RBCs containing predominately oxy and methemoglobin were investigated using three different lasers of wavelengths 473 nm, 533 nm, and 633 nm. The single cell study showed that half of the oxy-RBC and meth-RBC exhibit oxygen saturation of about 90 % and 38.61 % respectively.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.206
Teacher spread0.196 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

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