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Record W2793784994 · doi:10.1117/12.2290619

Computational circular dichroism estimation for point-of-care diagnostics via vortex half-wave retarders

2018· article· en· W2793784994 on OpenAlexaff
Shahid A. Haider, Megan Y. Tran, Alexander Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPolarizerPolarimetryComputer scienceContext (archaeology)RetarderCircular polarizationPoint (geometry)OpticsPoint of careElectronic engineeringMaterials sciencePhysicsBirefringenceEngineeringMathematics

Abstract

fetched live from OpenAlex

Observing the circular dichroism (CD) caused by organic molecules in biological fluids can provide powerful indicators of patient health and provide diagnostic clues for treatment. Methods for this kind of analysis involve tabletop devices that weigh tens of kilograms with costs on the order of tens of thousands of dollars, making them prohibitive in point-of-care diagnostic applications. In an e ort to reduce the size, cost, and complexity of CD estimation systems for point-of-care diagnostics, we propose a novel method for CD estimation that leverages a vortex half-wave retarder in between two linear polarizers and a two-dimensional photodetector array to provide an overall complexity reduction in the system. This enables the measurement of polarization variations across multiple polarizations after they interact with a biological sample, simultaneously, without the need for mechanical actuation. We further discuss design considerations of this methodology in the context of practical applications to point-of-care diagnostics.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.519

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)0.0000.000

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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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