{"id":"W2330545593","doi":"10.1109/lawp.2015.2487381","title":"60-GHz Statistical Channel Characterization for Wireless Data Centers","year":2015,"lang":"en","type":"article","venue":"IEEE Antennas and Wireless Propagation Letters","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; University of Ottawa; Université du Québec en Outaouais","funders":"","keywords":"Path loss; Extremely high frequency; Channel (broadcasting); Interference (communication); Radio propagation model; Log-distance path loss model; Link budget; Computer science; Wireless; Shadow mapping; Delay spread; Software deployment; Electronic engineering; Radio propagation; Computer network; Fading; Telecommunications; Engineering; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005252045,0.0004366879,0.0002817344,0.0009167503,0.000489894,0.000493716,0.0004784621,0.0002911965,0.0007190296],"category_scores_gemma":[0.001532436,0.0001785178,0.0002005436,0.0008826955,0.0002731713,0.0004877372,0.0003253217,0.0003382102,0.0002752762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008759721,"about_ca_system_score_gemma":0.0007032037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003967822,"about_ca_topic_score_gemma":0.003939473,"domain_scores_codex":[0.999518,0.00007979003,0.00001791798,0.0001006295,0.0001945866,0.00008906426],"domain_scores_gemma":[0.9990277,0.0003517471,0.0001829854,0.0001691322,0.0002203503,0.00004820341],"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":[0.0004457934,0.0003213785,0.1231431,0.0001932463,0.00008719337,0.0004268798,0.0004244567,0.6047791,0.191708,0.004886993,0.002240369,0.07134361],"study_design_scores_gemma":[0.00001473358,0.0003067298,0.03565751,0.000008307571,0.00002746203,0.0003315944,0.0002462874,0.8991032,0.06099202,0.001068407,0.002192719,0.00005104948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8411327,0.0001169866,0.1543224,0.00009677351,0.00001707229,0.00006127294,0.0009651507,0.0009827096,0.002305013],"genre_scores_gemma":[0.9932083,0.00005439114,0.006158075,0.00001229795,0.000005580781,0.00002830022,0.0003113525,0.00003378022,0.0001877867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003967822,"threshold_uncertainty_score":0.00788945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05689552215965547,"score_gpt":0.2571412661721179,"score_spread":0.2002457440124624,"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."}}