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Record W2060165924 · doi:10.1364/ao.44.002033

Spectrally programmable light engine for in vitro or in vivo molecular imaging and spectroscopy

2005· article· en· W2060165924 on OpenAlexaff
Nicholas MacKinnon, Ulrich Stange, Pierre Lane, Calum MacAulay, Mathieu Quatrevalet

Bibliographic record

VenueApplied Optics · 2005
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsOccupational Cancer Research Centre
Fundersnot available
KeywordsOpticsMultispectral imageHyperspectral imagingMaterials scienceSpectroscopyImaging spectroscopyMicroscopySpectral imagingWavelengthOptical coherence tomographyOptoelectronicsPhysicsRemote sensing

Abstract

fetched live from OpenAlex

A spectrally and temporally programmable light engine can create any spectral profile for hyperspectral, fluorescence, or principal-component imaging or with medical photonics devices employing spectroscopy, microscopy, and endoscopy. Multispectral imaging feasibility was demonstrated by capturing nine images at wavelengths from 450 to 650 mm (25-nm FWHM) with a CCD-camera-equipped bronchoscope coupled to the light engine. Selected wavelength regions were combined to produce a color endoscopy image.

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.253
Teacher spread0.247 · 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
Published2005
Admission routes1
Has abstractyes

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