MétaCan
Menu
Back to cohort
Record W2049166428 · doi:10.1364/oe.18.023796

Miniaturized multimodal CARS microscope based on MEMS scanning and a single laser source

2010· article· en· W2049166428 on OpenAlexafffund
Sangeeta Murugkar, Brett Smith, Prateek R. Srivastava, Adrian Moica, Majid Naji, Craig Brideau, Peter K. Stys, Hanan Anis

Bibliographic record

VenueOptics Express · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of CalgaryUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsOpticsMaterials scienceMicroscopeMicroelectromechanical systemsFemtosecondLaserLaser scanningMicroscopySecond-harmonic generationRaman scatteringOptoelectronicsRaman spectroscopyPhysics

Abstract

fetched live from OpenAlex

We demonstrate a novel miniaturized multimodal coherent anti-Stokes Raman scattering (CARS) microscope based on microelectromechanical systems (MEMS) scanning mirrors and custom miniature optics. A single Ti:sapphire femtosecond pulsed laser is used as the light source to produce the CARS, two photon excitation fluorescence (TPEF) and second harmonic generation (SHG) images using this miniaturized microscope. The high resolution and distortion-free images obtained from various samples such as a USAF target, fluorescent and polystyrene microspheres and biological tissue successfully demonstrate proof of concept, and pave the path towards future integration of parts into a handheld multimodal CARS probe for non- or minimally-invasive in vivo imaging.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.284
Teacher spread0.276 · 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

Citations39
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueOptics ExpressSame topicSpectroscopy Techniques in Biomedical and Chemical ResearchFrench-language works237,207