{"id":"W2886907797","doi":"10.1109/icc.2018.8423042","title":"Coverage Analysis of Decode-and-Forward Relaying in Millimeter Wave Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Stochastic geometry; Coverage probability; Relay; Transmitter; Computer science; Poisson point process; Decoding methods; Path loss; Signal-to-noise ratio (imaging); Point process; Topology (electrical circuits); Extremely high frequency; Monte Carlo method; Poisson distribution; Electronic engineering; Algorithm; Telecommunications; Mathematics; Statistics; Physics; Electrical engineering; Wireless; Engineering; Power (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002068915,0.00009961004,0.0002358223,0.0003553931,0.00002238002,0.00001477729,0.00004442468,0.00007149453,0.0002529045],"category_scores_gemma":[0.00001416453,0.00009153354,0.00007477165,0.0004841977,0.00002386833,0.00007617952,0.00002909853,0.00008745722,0.000005378727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002380603,"about_ca_system_score_gemma":0.000003110496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003272711,"about_ca_topic_score_gemma":0.0001871408,"domain_scores_codex":[0.9992889,0.00001742384,0.0003019432,0.0001394519,0.00008245312,0.000169792],"domain_scores_gemma":[0.9996847,0.00004795539,0.00002631596,0.0001537976,0.00003839602,0.00004883623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002950991,0.00002646785,0.01912923,0.00005363243,0.001196515,0.000005833757,0.001229917,0.9140427,0.01583022,0.0002154594,0.0002508809,0.04798967],"study_design_scores_gemma":[0.0001640102,0.00001785317,0.002647578,0.00001236042,0.0001264901,7.215668e-7,0.00002576596,0.9899867,0.006771229,0.00005583747,0.00007964156,0.00011177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4532334,0.0001234493,0.5424625,0.000005315706,0.00004974532,0.00003959797,0.000001463443,0.00003442668,0.00405012],"genre_scores_gemma":[0.9942304,0.0002138315,0.005319416,0.0001014192,0.00002826913,0.000003668938,0.000008551639,0.0000151047,0.00007929797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.540997,"threshold_uncertainty_score":0.3732629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01702838265248766,"score_gpt":0.2233112571330818,"score_spread":0.2062828744805941,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}