MétaCan
Menu
Back to cohort
Record W2055241533 · doi:10.1364/ao.48.004130

Comparison of the relationships between lidar integrated backscattered light and accumulated depolarization ratios for linear and circular polarization for water droplets, fog oil, and dust

2009· article· en· W2055241533 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Optics · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsDefence Research and Development CanadaRoyal Military College of Canada
Fundersnot available
KeywordsLidarOpticsScatteringPolarization (electrochemistry)Monte Carlo methodCircular polarizationMie scatteringLinear polarizationLight scatteringDepolarization ratioMaterials scienceDepolarizationPhysicsLaserChemistry

Abstract

fetched live from OpenAlex

Recently, an empirical relationship between the layer integrated backscattered light and the layer accumulated depolarization ratio has been established for linear polarization for the case of water droplet clouds. This is a powerful relation, allowing calibration of space lidar and correction of the lidar signal for multiple scattering effects. The relationship is strongly based on Monte Carlo simulations with some experimental evidence. We support the empirical relationship with strong experimental data and then show experimentally and via second order scattering theoretical calculations that a modified relationship can be obtained for circular polarization. Also, we demonstrate that other empirical relationships exist between the layer accumulated linear and circular depolarization ratios and the layer integrated backscattered light for submicrometer particles and nonspherical particles.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.364

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 head0.025
GPT teacher head0.259
Teacher spread0.235 · 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