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Record W4214831516 · doi:10.5194/amt-15-1093-2022

Laser imaging nephelometer for aircraft deployment

2022· article· en· W4214831516 on OpenAlexaff
Adam T. Ahern, Frank Erdesz, N. L. Wagner, C. A. Brock, Ming Lyu, Kyra Slovacek, Richard H. Moore, Elizabeth B. Wiggins, Daniel M. Murphy

Bibliographic record

VenueAtmospheric measurement techniques · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsUniversity of Alberta
FundersNational Oceanic and Atmospheric AdministrationNational Aeronautics and Space Administration
KeywordsNephelometerPolarimetryRemote sensingOpticsLidarPolarization (electrochemistry)WavelengthLaserLinear polarizationDetectorPhysicsEnvironmental scienceScatteringLight scatteringGeology

Abstract

fetched live from OpenAlex

Abstract. Validation of remote sensing retrievals of aerosol microphysical and optical properties requires in situ measurements of the same properties. We present here an improved imaging nephelometer for measuring the directionality and polarization of light (i.e., polarimetry) scattered at two wavelengths (405 and 660 nm) with high temporal resolution. The instrument was designed for airborne deployment and is capable of ground-based measurements as well. The laser imaging nephelometer (LiNeph) uses two orthogonal detectors with wide-angle lenses and linearly polarized light sources to measure both the phase function, P11(θ), and degree of linear polarization, -P12/P11(θ). In this work, we will describe the instrument function and calibration, as well as data acquisition and reduction. The instrument was first deployed aboard the NASA DC-8 during the 2019 FIREX-AQ campaign. Here, we present field measurements of smoke plumes that show that the LiNeph has sufficient resolution for 0.24 Hz polarimetric measurements at two wavelengths, 405 and 660 nm, at integrated scattering coefficients ranging from 50–8000 Mm−1.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.685
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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 designNot applicable
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

Citations8
Published2022
Admission routes1
Has abstractyes

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