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Record W2090126497 · doi:10.1117/12.839798

Characterization of microfluidic systems with Doppler optical coherence tomography

2009· article· en· W2090126497 on OpenAlexafffund
Lionel Carrion, E. Hamel, Antoine Leblanc-Hotte, Caroline Boudoux, Olivier T. Guenat, R. Maciejko

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaPolytechnique Montréal
KeywordsOptical coherence tomographyMicrofluidicsMicrochannelDoppler effectHomogeneity (statistics)OpticsMaterials scienceComputer scienceNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Doppler Optical Coherence Tomography (DOCT) is a biomedical imaging technique that allows simultaneous structural imaging and flow monitoring inside biological tissues and materials with spatial resolution in the micrometer scale. It has recently been applied to the characterization of microfluidic systems. Structural and flow imaging of novel microfluidics platforms for cytotoxicologic applications were obtained with a real-time, Near Infrared Spectral Domain DOCT system. Characteristics such as flow homogeneity in the chamber, which is one of the most important parameters for cell culture, are investigated. OCT and DOCT images were used to monitor flow inside a specific platform that is based on microchannel division for a better flow homogeneity. In particular, the evolution of flow profile at the transition between the microchannel structure and the chamber is studied.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.229
Teacher spread0.219 · 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
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

Citations6
Published2009
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicCoronary Interventions and DiagnosticsFrench-language works237,207