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Record W2135060006 · doi:10.1109/tbc.2011.2131430

An Empirical Comparative Study of Prediction Methods for Estimating Multipath Due to Signal Scattering From Wind Turbines on Digital TV Services

2011· article· en· W2135060006 on OpenAlexaff
Itziar Angulo, David Vega, Carlos Fernández, David Guerra, Yiyan Wu, Pablo Angueira, J.L. Ordiales

Bibliographic record

VenueIEEE Transactions on Broadcasting · 2011
Typearticle
Languageen
FieldEngineering
TopicRadio Wave Propagation Studies
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsMultipath propagationComputer scienceSIGNAL (programming language)Electronic engineeringTelecommunicationsRemote sensingEngineeringGeologyChannel (broadcasting)

Abstract

fetched live from OpenAlex

Several authors have theoretically studied the effect of wind turbines on the propagation of electromagnetic waves in the UHF band. The International Telecommunication Union also proposes a simplified model to evaluate the impact caused to television reception by a wind turbine in the Recommendation ITU-R BT.805. This paper presents an empirical study of the above-mentioned prediction methods for estimating signal scattering from wind turbines in the UHF band, comparing predicted values with empirical data obtained from a DTV measurement campaign carried out in Spain. As signal scattering is independent of the transmission standard or modulation, the results are applicable to any broadcasting and wireless communication signals in the UHF band that may be affected by the multipath interference caused by a wind farm.

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)0.013
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.066
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.090
GPT teacher head0.355
Teacher spread0.265 · 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 designSimulation or modeling
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

Citations15
Published2011
Admission routes1
Has abstractyes

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