MétaCan
Menu
Back to cohort

Standard Deviation of the Copolar Correlation Coefficient for Simultaneous Transmission and Reception of Vertical and Horizontal Polarized Weather Radar Signals

2003· article· en· W2178305067 on OpenAlexaff
E. Torlaschi, Yves Gingras

Bibliographic record

VenueJournal of Atmospheric and Oceanic Technology · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsStandard deviationPhysicsRadarCovarianceGaussianDoppler effectPolarization (electrochemistry)Covariance matrixCorrelation coefficientComputational physicsOpticsMathematicsStatisticsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

The perturbation method is used to derive the variance of the zero-lag copolar correlation coefficient, | ρHV | , for a radar simultaneously transmitting and receiving both horizontal and vertical polarization. The variance of | ρHV | is a function of its expected value, the number of samples, the normalized Doppler velocity spectrum width, συn, and the signal-to-noise ratio in the receivers. Assuming the covariances and cross covariance of the radar signals are represented by zero-mean complex Gaussian processes, the precision of the estimate of | ρHV | for alternate and simultaneous transmission and reception of horizontal and vertical polarization is discussed. Calculations show that variations in the expected value of | ρHV | from 0.7 to 0.98 lead to a decrease in the precision of the estimate of | ρHV | of more than one order of magnitude: for narrow spectra (συn < 0.04) the estimates do not depend on the sampling scheme used but on the number of samples taken, and at larger spectrum widths (συn > 0.1) the simultaneous scheme can be more than one order of magnitude more precise than the alternate scheme. Furthermore, for good precision the signal-to-noise ratio should exceed 10 dB.

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)0.000
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 score0.453
Threshold uncertainty score0.170

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Atmospheric and Oceanic TechnologySame topicPrecipitation Measurement and AnalysisFrench-language works237,207