{"id":"W3024363566","doi":"10.33832/ijdrbc.2019.10.02","title":"Business Success through Understanding Human Emotions: Case Study of Classifying Emotions using the Brain Waives EEG Data","year":2019,"lang":"en","type":"article","venue":"International journal of disaster recovery and business continuity","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Electroencephalography; Psychology; Cognitive psychology; Computer science; Neuroscience","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":[0.0005677601,0.000200154,0.0003544874,0.0002055062,0.0002661143,0.0004968235,0.001093512,0.00005553189,0.00004538177],"category_scores_gemma":[0.0003546861,0.0001396866,0.00007266667,0.0003975474,0.0001973921,0.002646483,0.0006614148,0.0002679658,9.465732e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008139827,"about_ca_system_score_gemma":0.00007502001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003135345,"about_ca_topic_score_gemma":0.0003496052,"domain_scores_codex":[0.9979112,0.0002940708,0.0007498466,0.0003600692,0.0005018046,0.0001829992],"domain_scores_gemma":[0.9974522,0.0006912571,0.0008837222,0.0003909699,0.0005406258,0.00004122312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","study_design_scores_codex":[0.001983706,0.009863688,0.3949487,0.0009219457,0.002571453,0.005491194,0.07369413,0.02514416,0.4480282,0.009887518,0.001194433,0.02627084],"study_design_scores_gemma":[0.03152969,0.002740345,0.3713321,0.01096313,0.001481925,0.09030185,0.389739,0.05960866,0.009302511,0.02626131,0.00295432,0.003785189],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9753784,0.00004608749,0.02124708,0.0008232484,0.001972594,0.0002634904,0.00007081932,0.00001138552,0.0001868893],"genre_scores_gemma":[0.9992698,0.00002826077,0.0001645652,0.0002028147,0.0002435295,8.840421e-7,0.000004790291,0.00001582766,0.00006954774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4387257,"threshold_uncertainty_score":0.5696253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1546529025757024,"score_gpt":0.3661401571200856,"score_spread":0.2114872545443832,"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."}}