{"id":"W2741203640","doi":"10.1109/icc.2017.7996993","title":"Wireless noise prevention for mobile agents in smart home","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Guelph","funders":"","keywords":"Wireless sensor network; Computer science; Wireless; Home automation; Computer network; Wi-Fi array; Key distribution in wireless sensor networks; Noise (video); Wireless network; Interference (communication); Fixed wireless; Real-time computing; Embedded system; Telecommunications; Artificial intelligence; Channel (broadcasting)","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":[0.0003674606,0.0003469035,0.0005092138,0.0002722958,0.0004245114,0.0005661559,0.0006038803,0.0006084081,0.0006962572],"category_scores_gemma":[0.001206723,0.0001810393,0.0003477608,0.0001671927,0.0003554085,0.0009496069,0.0008737709,0.000323616,0.0002935536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002737741,"about_ca_system_score_gemma":0.0003865324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001683716,"about_ca_topic_score_gemma":0.001708947,"domain_scores_codex":[0.9997266,0.00009585564,0.00001730389,0.00005847904,0.00006058589,0.00004129192],"domain_scores_gemma":[0.9996853,0.0001176048,0.00005768995,0.00004964247,0.00006416474,0.00002569986],"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":[0.0007569057,0.0005230887,0.008866047,0.000354113,0.0001243214,0.001259542,0.0009925101,0.4510745,0.05861942,0.04582419,0.004134341,0.427471],"study_design_scores_gemma":[0.00001758496,0.0001825519,0.000576214,0.00001478245,0.00003135396,0.0001539336,0.0001118231,0.9862658,0.00461758,0.004735263,0.003280649,0.00001252293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08192262,0.0006650833,0.9123829,0.0002172546,0.00008863532,0.00008316519,0.00001778925,0.0006559758,0.003966537],"genre_scores_gemma":[0.9368979,0.0002879366,0.0594796,0.00006900612,0.00002974147,0.00007661094,0.00002162978,0.00002148967,0.00311619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001683716,"threshold_uncertainty_score":0.003347814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0750133790096574,"score_gpt":0.3381149378692012,"score_spread":0.2631015588595438,"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."}}