MétaCan
Menu
Back to cohort
Record W1965480054 · doi:10.1117/12.434504

<title>Experimental tests of a two-layer Monte Carlo-diffusion hybrid model for photon migration in the frequency domain</title>

2001· article· en· W1965480054 on OpenAlexaff
Gregory W. Faris, George Alexandrakis, David R. Busch, Michael S. Patterson

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsMcMaster UniversityHamilton Regional Laboratory Medicine Program
FundersNational Institutes of Health
KeywordsMonte Carlo methodDiffusionAttenuation coefficientOpticsFrequency domainPhotonPhoton diffusionAbsorption (acoustics)Layer (electronics)Materials scienceComputational physicsPhysicsComputer scienceMathematics

Abstract

fetched live from OpenAlex

We examine the ability to recover the optical properties of a two-layer turbid medium using multi-distance frequency domain reflectance measurements and a hybrid Monte Carlo-- diffusion model. Frequency domain measurements are performed on two-layer liquid tissue phantoms simulating skin on muscle and skin on fat. Particular care to systematic effects in the photomultiplier is required for the measurements at short source-detectors distances. The model converges when fitting five free parameters (the optical properties of the upper and lower layers and the upper layer thickness). However, discrepancies between experimental and model yield insufficient accuracy for the absorption coefficient of the upper layer.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.014
GPT teacher head0.277
Teacher spread0.263 · 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 designSimulation or modeling
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

Citations1
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Imaging and Spectroscopy TechniquesFrench-language works237,207