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Record W2504850934 · doi:10.1080/02786826.2016.1219691

Assessing the accuracy of complex refractive index retrievals from single aerosol particle cavity ring-down spectroscopy

2016· article· en· W2504850934 on OpenAlexafffund
Michael I. Cotterell, Thomas C. Preston, Andrew J. Orr‐Ewing, Jonathan P. Reid

Bibliographic record

VenueAerosol Science and Technology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsMcGill University
FundersNatural Environment Research CouncilEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaUniversity of Bristol
KeywordsAerosolCavity ring-down spectroscopyRefractive indexParticle (ecology)RADIUSOpticsSpectroscopyMie scatteringExtinction (optical mineralogy)Absorption (acoustics)ChemistryMolecular physicsMaterials scienceLaserScatteringLight scatteringPhysics

Abstract

fetched live from OpenAlex

Cavity ring-down spectroscopy (CRDS) of single, optically manipulated aerosol particles affords quantitative retrieval of refractive indices for particles of fixed or evolving composition with high precision. Here, we quantify the accuracy with which refractive index determinations can be made by CRDS for single particles confined within the core of a Bessel laser beam and how that accuracy is degraded as the particle size is progressively reduced from the coarse mode (&gt;1 μm radius) to the accumulation mode (&lt;500 nm radius) regime. We apply generalized Lorenz–Mie theory to the intra-cavity standing wave to explore the effect of particle absorption on the distribution of extinction cross section determinations resulting from stochastic particle motion in the Bessel beam trap. The analysis provides an assessment of the accuracy with which the real, <i>n</i>, and imaginary, κ, components of the refractive index can be determined for a single aerosol particle. Published with license by American Association for Aerosol Research Read the transcript Watch the video on Vimeo

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.047
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.022
GPT teacher head0.287
Teacher spread0.266 · 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.

Study designBench or experimental
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

Citations30
Published2016
Admission routes2
Has abstractyes

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