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Record W4313475349 · doi:10.1364/optcon.473102

PyMieSim: an open-source library for fast and flexible far-field Mie scattering simulations

2023· article· en· W4313475349 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOptics Continuum · 2023
Typearticle
Languageen
FieldEngineering
TopicOptical Polarization and Ellipsometry
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMie scatteringDetectorScatteringOpticsRangingPython (programming language)Coherence (philosophical gambling strategy)Optical coherence tomographyPhysicsComputer scienceNear and far fieldLight scattering

Abstract

fetched live from OpenAlex

Mie scattering simulations are essential to designing sensing and imaging devices for atmospheric and biomedical sciences applications. Current simulators based on Lorenz-Mie theory allow obtaining scattering phase functions for several scatterer geometries. Still, they are not optimized for simulating ranging parameters and do not compute coupling powers into coherent or incoherent detectors. Here, we present PyMieSim: a Python interface wrapping a C++ parallelized core for a fast, flexible, and highly visual simulation package, allowing for the study of ranges of parameters for different source, scatterer, and detector configurations. We demonstrate two applications: flow-cytometry geometry optimization and few-mode optical coherence tomography contrast analysis.

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 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 score0.476
Threshold uncertainty score0.550

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 communication0.0000.001
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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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