{"id":"W4323644194","doi":"10.1109/tnsre.2023.3254151","title":"Artificial Proprioceptive Reflex Warning Using EMG in Advanced Driving Assistance System","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; Korea Creative Content Agency; Ministry of Culture, Sports and Tourism","keywords":"Advanced driver assistance systems; Computer science; Perception; Warning system; Action (physics); Computer vision; Artificial intelligence; Simulation; Psychology; Telecommunications","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":[0.0002698217,0.000184538,0.0002343995,0.0003964476,0.0001967215,0.00009497369,0.0000870638,0.00007241509,0.000001101509],"category_scores_gemma":[0.00006497923,0.0001717725,0.00006332686,0.0007052522,0.00004407171,0.0003402371,0.000002487154,0.0002565652,0.000006601107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001666247,"about_ca_system_score_gemma":0.00001275832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002713652,"about_ca_topic_score_gemma":0.00001052876,"domain_scores_codex":[0.998484,0.0001231514,0.0004280783,0.0004496211,0.0002238944,0.0002912917],"domain_scores_gemma":[0.9989576,0.0007178063,0.00008098184,0.0001443731,0.00003253691,0.00006668607],"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":[0.00001403587,0.00001393427,0.00005006951,0.0002151153,0.000002570726,0.000007832842,0.0009903419,0.6749949,0.3212067,0.0001113071,0.000001173077,0.002391941],"study_design_scores_gemma":[0.0001798785,0.0001452971,0.001453991,0.0007961232,0.00000522839,0.0000253023,0.001440968,0.9704835,0.025215,0.000004155911,0.00004409361,0.0002063962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.918002,0.00002155677,0.07955767,0.00009131075,0.001484696,0.0004244243,0.00000756378,0.0003934034,0.00001734308],"genre_scores_gemma":[0.9992095,0.000003707902,0.0005560438,0.000006366015,0.00005159517,0.00008092565,3.437985e-7,0.00002907891,0.00006242787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2959917,"threshold_uncertainty_score":0.7004681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02372003227933183,"score_gpt":0.270970308815574,"score_spread":0.2472502765362422,"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."}}