MétaCan
Menu
Back to cohort
Record W1985627406 · doi:10.1366/000370208784658174

Assessment of Near-Infrared Path Length in Fibrous Phantom and Muscle Tissue

2008· article· en· W1985627406 on OpenAlexaff
Eugene Gussakovsky, V.V. Kupriyanov

Bibliographic record

VenueApplied Spectroscopy · 2008
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsNational Research Council CanadaNational Research Council Institute for Biodiagnostics
FundersGenomic Health
KeywordsImaging phantomPath lengthOpticsMaterials scienceNear-infrared spectroscopyAbsorption (acoustics)InfraredBiomedical engineeringChemistryAnalytical Chemistry (journal)PhysicsMedicine

Abstract

fetched live from OpenAlex

The first derivative of the pseudo-absorption spectrum of a water-loaded cotton wool (water-CW) phantom, which mimics muscle tissues, was used to determine the light path length in the near-infrared (NIR) region. The light path length increased as the density of the turbid medium decreased. It is independent of both water content in the range of 75-85% (by weight) and the diffuse reflecting reference used to determine the pseudo-absorbance. The path length determination procedure was verified by measurements of diffuse reflectance in chicken breast tissue for which the path length of 1.8 mm (differential path length factor, DPF = 2.1) was found to be similar to the path length of NIR light of 1.5-2.2 mm (DPF = 1.8-2.6) in a water-CW phantom of density similar to chicken breast. We conclude that the NIR light path length can serve as a characteristic of muscle tissue density.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.311
Teacher spread0.300 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueApplied SpectroscopySame topicOptical Imaging and Spectroscopy TechniquesFrench-language works237,207