{"id":"W2332709574","doi":"10.1109/twc.2016.2547378","title":"Multimedia Content Delivery in Millimeter Wave Home Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Resource allocation; Bandwidth (computing); Heuristic; Convex optimization; Wireless; Mathematical optimization; Optimization problem; Wireless network; Integer programming; Computer network; Distributed computing; Algorithm; Telecommunications; Regular polygon; Artificial intelligence; Mathematics","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":[0.0001574395,0.0002065645,0.0002191476,0.0002875459,0.0001578847,0.00002673883,0.0003910929,0.0001360751,0.0001030235],"category_scores_gemma":[0.000003205743,0.00017519,0.0001201443,0.0002560607,0.0001092905,0.0002049525,0.000005642946,0.0003582934,0.0001013028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001757515,"about_ca_system_score_gemma":0.00001757221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004948858,"about_ca_topic_score_gemma":0.0004739685,"domain_scores_codex":[0.9988186,0.0001050122,0.000439384,0.0001979907,0.000137471,0.0003015052],"domain_scores_gemma":[0.9982872,0.0003678096,0.00003901053,0.001107264,0.00008442085,0.0001143232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005165472,0.0004311434,0.00008654421,0.00002874114,0.0001858287,0.00000495174,0.0008493906,0.1647308,0.1510749,0.0000727483,0.0001602515,0.682323],"study_design_scores_gemma":[0.001181799,0.0000366088,0.0003201606,0.0002117015,0.00003239223,0.000006500828,0.00008960474,0.9501684,0.04716779,0.00004640484,0.0003533543,0.0003852514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1269626,0.000380722,0.8711027,0.0003507921,0.0003591089,0.0002614917,0.00004355421,0.0002704706,0.0002685154],"genre_scores_gemma":[0.9893156,0.005070733,0.005034707,0.0001181066,0.00002051532,0.0002109745,0.00001032059,0.0000528765,0.0001661329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.866068,"threshold_uncertainty_score":0.7144039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05816597491839962,"score_gpt":0.2347861389077901,"score_spread":0.1766201639893905,"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."}}