{"id":"W2071549308","doi":"10.1109/qbsc.2014.6841208","title":"EMI-aware central processing point deployment for wireless networks in hospitals","year":2014,"lang":"en","type":"article","venue":"","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; EMI; Wireless; Integer programming; Software deployment; Throughput; Linear programming; MATLAB; Transmitter power output; Electromagnetic interference; Interference (communication); Interior point method; Transmitter; Mathematical optimization; Computer network; Algorithm; Telecommunications; 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":[0.0002208009,0.0002474886,0.0002959391,0.00007213166,0.00005794905,0.00007585655,0.0001984688,0.0001619472,0.00002381557],"category_scores_gemma":[0.0000114178,0.0002420685,0.00007241961,0.0001895591,0.00002534622,0.0002025744,0.00003852962,0.000203002,0.000004930291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001641995,"about_ca_system_score_gemma":0.00001493699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001911517,"about_ca_topic_score_gemma":0.0001857604,"domain_scores_codex":[0.9984308,0.00002023565,0.0003600519,0.0002743902,0.0001336427,0.0007808694],"domain_scores_gemma":[0.9994834,0.00008363315,0.0000375152,0.0002217943,0.00003659838,0.0001370603],"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":[0.00001638279,0.00003630144,0.009360987,0.0001894619,0.00002161452,0.00000377207,0.0002644267,0.8670018,0.0001092264,0.0008725498,0.003962339,0.1181611],"study_design_scores_gemma":[0.0006056685,0.0000343671,0.00687641,0.0001762765,0.000009145084,0.000002284633,0.00008093278,0.9903075,0.0007892071,0.0001928918,0.0005907826,0.00033458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2846816,0.0002763385,0.7118229,0.0001072279,0.0004390372,0.0005265548,0.000002412653,0.0006484028,0.001495452],"genre_scores_gemma":[0.9975156,0.00003534252,0.00156218,0.0001141833,0.0004184275,0.0001631602,0.00002328811,0.00009936195,0.00006847629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7128339,"threshold_uncertainty_score":0.9871266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005040542366441117,"score_gpt":0.2026675004148925,"score_spread":0.1976269580484514,"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."}}