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Record W2331262350 · doi:10.1021/ac401864g

Combined Stokes Vector and Mueller Matrix Polarimetry for Materials Characterization

2013· article· en· W2331262350 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnalytical Chemistry · 2013
Typearticle
Languageen
FieldEngineering
TopicOptical Polarization and Ellipsometry
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMueller calculusPolarimeterPolarimetryStokes parametersPolarization (electrochemistry)Characterization (materials science)IsotropyMatrix (chemical analysis)ChemistryOpticsPhysicsScatteringChromatography

Abstract

fetched live from OpenAlex

A robust way of measuring the optical properties of any material is to interrogate it with light of different polarizations. The 16-element Mueller matrix provides the most complete description of the optical properties of a sample based on its ability to alter the polarization state of transmitted or reflected light. This is valuable for ordered and isotropic materials alike. Similarly, the 4-element Stokes vector is the most complete description of the polarization of a light beam, including any depolarization effects. While the Mueller matrix offers the most chemical and physical insight, the Stokes vectors are easier to obtain, and there are more schemes for measuring them quickly in situations where time resolution is important. We describe a method where any Stokes polarimeter may be adapted to obtain Mueller matrices and discuss various approaches for achieving better time resolution.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.217
Teacher spread0.211 · 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