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Record W2939966914 · doi:10.1117/1.jbo.24.4.046502

Excitation parameters optimized for coherent anti-Stokes Raman scattering imaging of myelinated tissue

2019· article· en· W2939966914 on OpenAlexafffund
Craig Brideau, Kelvin W. C. Poon, Pina Colarusso, Peter K. Stys

Bibliographic record

VenueJournal of Biomedical Optics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta InnovatesMultiple Sclerosis SocietyCanada Research ChairsMultiple Sclerosis Society of Canada
KeywordsRaman scatteringMicroscopyScatteringMaterials scienceOpticsRaman spectroscopyLaserExcitationResolution (logic)Nuclear magnetic resonancePhysicsComputer science

Abstract

fetched live from OpenAlex

Coherent anti-Stokes Raman scattering (CARS) generates a strong label-free signal in the long wavenumber C─H stretching region. Lipid-rich myelinated tissues, such as brain and spinal cord, would appear to be ideal subjects for imaging with CARS laser-scanning microscopy. However, the highly ordered, biochemically complex, and highly scattering nature of such tissues complicate the use of the technique. A CARS microscopy approach is presented that overcomes the challenges of imaging myelinated tissue to achieve chemically and orientationally sensitive high-resolution images.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.015
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.328
Teacher spread0.317 · 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.

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

Citations8
Published2019
Admission routes2
Has abstractyes

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