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Record W2030379029 · doi:10.1364/ol.31.001809

Simple relation between lidar multiple scattering and depolarization for water clouds

2006· article· en· W2030379029 on OpenAlexaff
Yongxiang Hu, Zhaoyan Liu, David M. Winker, Mark Vaughan, Vincent Noël, Luc Bissonnette, G. Roy, Matthew J. McGill

Bibliographic record

VenueOptics Letters · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsLidarMonte Carlo methodScatteringBackscatter (email)Extinction (optical mineralogy)Mie scatteringOpticsForward scatterPhysicsRemote sensingComputational physicsLight scatteringRange (aeronautics)Atmospheric opticsMaterials scienceGeologyStatisticsMathematics

Abstract

fetched live from OpenAlex

An empirical relationship is derived between the multiple-scattering fraction and the linear depolarization ratio by using Monte Carlo simulations of water clouds measured by backscatter lidar. This relationship is shown to hold for clouds having a wide range of extinction coefficients, mean droplet sizes, and droplet size distribution widths. The relationship is also shown to persist for various instrument fields of view and for measurements made within broken cloud fields. The results obtained from the Monte Carlo simulations are verified by using multiple-field-of-view lidar measurements. For space-based lidars equipped to measure linear depolarization ratios, this new relationship can be used to accurately assess signal perturbations due to multiple scattering within nonprecipitating water clouds.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.199
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations117
Published2006
Admission routes1
Has abstractyes

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