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Record W1998736582 · doi:10.1002/jrs.878

The power distribution advantage of fiber‐optic coupled ultraviolet resonance Raman spectroscopy for bioanalytical and biomedical applications

2002· article· en· W1998736582 on OpenAlexaff
Christopher Barbosa, Frédéric H. Vaillancourt, Lindsay D. Eltis, Michael W. Blades, Robin F. B. Turner

Bibliographic record

VenueJournal of Raman Spectroscopy · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRaman spectroscopySpectrometerOptical fiberResonance Raman spectroscopyFiberContext (archaeology)UltravioletResonance (particle physics)Substrate (aquarium)ChemistrySpectroscopyMaterials scienceOptoelectronicsAnalytical Chemistry (journal)OpticsPhysicsChromatographyAtomic physics

Abstract

fetched live from OpenAlex

Abstract Fiber‐optic coupled ultraviolet resonance Raman spectroscopy (FO‐UVRRS) of photosensitive biological samples is discussed in the context of both clinical and basic medical research applications. The fiber‐optic probes are designed specifically for resonance Raman spectroscopy and offer a power distribution advantage over conventional focusing UVRR spectrometers that allows the use of higher power levels without increasing the risk of photo‐damage. A typical fiber‐optic probe using a 600 μm core diameter fiber for illumination of the sample allows an increase in power at the sample of over an order of magnitude while maintaining the same power density, and therefore the same level of safety, as a typical conventional UVRR spectrometer. This allows high‐quality spectra to be easily obtained in 10 s. Spectra of representative biological samples using high power levels without damage are presented, including the study of a photosensitive enzyme–substrate system under anaerobic conditions. Copyright © 2002 John Wiley & Sons, Ltd.

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 score0.239
Threshold uncertainty score0.610

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.305
Teacher spread0.297 · 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

Citations24
Published2002
Admission routes1
Has abstractyes

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