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Record W4386469396 · doi:10.32920/24092103

Reflectance of Biological Turbid Tissues under Wide Area Illumination: Single Backward Scattering Approach

2023· preprint· en· W4386469396 on OpenAlexaff
Alexandre Douplik, Gennadi Saiko

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsScatteringOpticsDiffusionHeavy traffic approximationSpectral lineReflectivityComputational physicsPhysicsDiffuse reflectionLight scatteringForward scatterBoundary (topology)MathematicsMathematical analysisQuantum mechanics

Abstract

fetched live from OpenAlex

Various scenarios of light propagation paths in turbid media (single backward scattering, multiple backward scattering, banana shape) are discussed and their contributions to reflectance spectra are estimated. It has been found that a single backward or multiple forward scattering quasi-1D paths can be the major contributors to reflected spectra in wide area illumination scenario. Such a single backward scattering (SBS) approximation allows developing of an analytical approach which can take into account refractive index mismatched boundary conditions and multilayer geometry and can be used for real-time spectral processing. The SBS approach can be potentially applied for the distances between the transport and reduced scattering domains. Its validation versus the Kubelka-Munk model, path integrals, and diffusion approximation of the radiation transport theory is discussed.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.141
GPT teacher head0.368
Teacher spread0.227 · 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
GenreMethods

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

Citations0
Published2023
Admission routes1
Has abstractyes

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