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Record W1998792870 · doi:10.1163/156939300x01373

Differential Rain Attenuation Statistics Including an Accurate Estimation of the Effective Slant Path Lengths - Abstract

2000· article· en· W1998792870 on OpenAlexaboutno aff
J.D. Kanellopoulos, Athanasios D. Panagopoulos, Spiros N. Livieratos

Bibliographic record

VenueJournal of Electromagnetic Waves and Applications · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAttenuationPath (computing)Path lengthStatisticsDifferential (mechanical device)EstimationRemote sensingComputer scienceMathematicsEnvironmental scienceOpticsPhysicsGeology

Abstract

fetched live from OpenAlex

The differential rain attenuation is considered to be one of the main propagation factors causing interference between adjacent Earth-Space paths. In the present paper, an existing method to predict the differential rain attenuation statistics is properly modified to include a more complicated but accurate estimation of the slant path lengths affected by the rain medium. The modified method is oriented to be generally applicable, particularly in the case of satellite paths operating under low elevation angles in the Ku-band, where the accurate estimation of the effective slant path lengths is very critical. The present results are compared with a set of available simulated data for the differential attenuation over pairs of paths located in Montreal. The influence of various other parameters such as the geographic latitude and climatic zone is also examined.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.251
Teacher spread0.238 · 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 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
Published2000
Admission routes1
Has abstractyes

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