{"id":"W2142250635","doi":"10.1016/j.bbr.2015.07.041","title":"The neuroergonomic evaluation of human machine interface design in air traffic control using behavioral and EEG/ERP measures","year":2015,"lang":"en","type":"article","venue":"Behavioural Brain Research","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"ALARM; Task (project management); Interface (matter); Electroencephalography; Salient; Computer science; Psychology; Artificial intelligence; Engineering; 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":[0.009911583,0.0001371404,0.0002079401,0.0003454098,0.0002623935,0.00005583587,0.0002800443,0.0001006847,0.0002962064],"category_scores_gemma":[0.0003927588,0.0001106322,0.00005180611,0.0002313762,0.0002513515,0.0001492095,0.00005866227,0.0005543982,0.00003149167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003023501,"about_ca_system_score_gemma":0.0001642369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001671662,"about_ca_topic_score_gemma":0.001297595,"domain_scores_codex":[0.9934908,0.004493956,0.0005132186,0.0003036055,0.000818268,0.0003801905],"domain_scores_gemma":[0.9981826,0.0006321945,0.0001269735,0.0003371725,0.0005878445,0.0001331951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01045217,0.00497206,0.3417082,0.00005622757,0.0003447084,0.0001432226,0.0840605,0.04516236,0.1556112,0.006996846,0.036868,0.3136245],"study_design_scores_gemma":[0.00955533,0.001305687,0.8898391,0.00006616167,0.00008461437,0.00007636977,0.008656777,0.08850468,0.0008455347,0.0002801094,0.0004586224,0.0003270292],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968016,0.0003353,0.0002132071,0.001030835,0.000277474,0.0009464694,0.00001116688,0.00003116824,0.0003527509],"genre_scores_gemma":[0.9992449,0.000001644758,0.00003819671,0.00002875136,0.00002565015,0.000104125,0.000006462973,0.00002330502,0.0005269648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5481309,"threshold_uncertainty_score":0.4511448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4806054791995061,"score_gpt":0.5412542909046989,"score_spread":0.06064881170519276,"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."}}