MétaCan
Menu
Back to cohort
Record W3013468025 · doi:10.1088/1674-1056/ab8373

Non-Gaussian statistics of partially coherent light in atmospheric turbulence*

2020· article· en· W3013468025 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChinese Physics B · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsDalhousie University
FundersNatural Science Foundation of Shandong ProvinceGovernment of Jiangsu ProvinceChina Postdoctoral Science FoundationNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPhysicsTurbulenceCoherence (philosophical gambling strategy)Atmospheric turbulenceGaussianDetectorNoise (video)OpticsStatistical physicsComputational physicsIntensity (physics)Degree of coherenceQuantum mechanicsMechanics

Abstract

fetched live from OpenAlex

We derive theoretically and verify experimentally a concise general expression for the normalized intensity correlations (IC) of partially coherent light in a weak atmospheric turbulence in the fast detector measurement regime. The derived relation reveals that the medium turbulence acts, in general, as an additional noise source enhancing the IC of partially coherent beams. The maximum of the beam IC is, in general, enhanced, causing the fields to exhibit super-Gaussian statistics. On the other hand, the relation indicates that turbulence-induced noise is negligible for sufficiently low coherence light, which reveals the condition for the turbulence-free correlation imaging.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.542

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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