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Record W2073161911 · doi:10.1080/09500340.2010.506010

Coherent Raman spectroscopy through one-dimensional random scattering medium

2010· article· en· W2073161911 on OpenAlexaff
Thomas M. Drane, J. W. Hepburn, Valery Milner

Bibliographic record

VenueJournal of Modern Optics · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRaman scatteringOpticsCoherent anti-Stokes Raman spectroscopyScatteringRaman spectroscopySpectroscopyX-ray Raman scatteringCoherent spectroscopyLaserMaterials sciencePhase (matter)PhysicsTransmission (telecommunications)Resolution (logic)Spectral resolutionSpectral lineComputational physicsTelecommunicationsComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

We investigate the possibility of implementing coherent anti-Stokes Raman spectroscopy (CARS) with a single laser beam passed through a one-dimensional scattering object. The effect of the random scattering is emulated by shaping the laser pulses with a spectral mask corresponding to the transmission spectrum of a random layered medium. Raman resonances are retrieved through correlation analysis of the CARS spectrum. We study the effect of the scattering parameters on the resolution of the method, and show that improvement of the spectroscopic sensitivity can be achieved by compensating the phase distortions introduced by the scatterer.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.015
GPT teacher head0.318
Teacher spread0.303 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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