{"id":"W2032377220","doi":"10.1155/2015/315948","title":"Internet of Vehicles for E-Health Applications in View of EMI on Medical Sensors","year":2015,"lang":"en","type":"article","venue":"Journal of Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National High-tech Research and Development Program; National Natural Science Foundation of China","keywords":"EMI; Wireless; Schedule; Computer science; Electromagnetic interference; Interference (communication); Nash equilibrium; Transmission (telecommunications); Computer network; Real-time computing; Telecommunications; Mathematical optimization; Channel (broadcasting); 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":[0.0007175991,0.00009528289,0.0004117735,0.0001790434,0.000006455453,0.000003331945,0.0001701321,0.00009504159,0.00001093431],"category_scores_gemma":[0.0001621547,0.00008123164,0.0000874128,0.0001691439,0.00004613441,0.00004739821,0.00001285179,0.0002586139,0.000001790669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001263263,"about_ca_system_score_gemma":0.0001132147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001502836,"about_ca_topic_score_gemma":0.00001587805,"domain_scores_codex":[0.9986417,0.0000457096,0.0007543474,0.00006411632,0.0003388658,0.0001552361],"domain_scores_gemma":[0.9990139,0.000239462,0.0002773748,0.0001195882,0.0001345616,0.0002151064],"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":[0.000447231,0.0005186818,0.009602979,0.001694089,0.0003712811,0.000041649,0.006755469,0.8710702,0.001306491,0.003175881,0.04738966,0.05762637],"study_design_scores_gemma":[0.01144317,0.003551421,0.027068,0.00957925,0.0001753587,0.00045733,0.00577781,0.7900822,0.04514742,0.002839138,0.1026889,0.001190047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953994,0.0007938297,0.002643075,0.000414326,0.0002027915,0.0002218463,0.000008342871,0.00001461953,0.0003018259],"genre_scores_gemma":[0.9980069,0.0002728612,0.001479946,0.0000415075,0.0001507845,0.000005555808,0.00000190776,0.00002230204,0.00001828212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08098802,"threshold_uncertainty_score":0.331253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02835912086854156,"score_gpt":0.2881736492348988,"score_spread":0.2598145283663573,"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."}}