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Record W1977919076 · doi:10.1364/oe.17.002984

Optimally chirped multimodal CARS microscopy based on a single Ti:sapphire oscillator

2009· article· en· W1977919076 on OpenAlexafffund
Adrian F. Pegoraro, Andrew Ridsdale, Douglas J. Moffatt, Yiwei Jia, John Paul Pezacki, Albert Stolow

Bibliographic record

VenueOptics Express · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsSteacie Institute for Molecular SciencesQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsOpticsMaterials scienceFemtosecondLaserRaman scatteringMicroscopySapphireChirpRaman spectroscopyFiber laserOptoelectronicsPhotonic-crystal fiberWavelengthPhysics

Abstract

fetched live from OpenAlex

We demonstrate high performance coherent anti-Stokes Raman scattering (CARS) microscopy of live cells and tissues with user-variable spectral resolution and broad Raman tunability (2500 - 4100 cm(-1)), using a femtosecond Ti:Sapphire pump and photonic crystal fiber output for the broadband synchronized Stokes pulse. Spectral chirp of the fs laser pulses was a user-variable parameter for optimization in a spectral focusing implementation of multimodal CARS microscopy. High signal-to-noise, high contrast multimodal imaging of live cells and tissues was achieved with pixel dwell times of 2-8 micros and low laser powers (< 30 mW total).

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.303
Teacher spread0.293 · 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

Citations193
Published2009
Admission routes2
Has abstractyes

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