{"id":"W3101690823","doi":"10.1155/2020/5640784","title":"Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Foundation of the Graduate Innovation Center, Nanjing University of Aeronautics and Astronautics; Nanjing University of Aeronautics and Astronautics; Nanjing University; Government of Jiangsu Province","keywords":"Identification (biology); Cognition; Cockpit; Computer science; Preprocessor; Cognitive load; Flight simulator; Process (computing); Data pre-processing; Simulation; Artificial intelligence; Engineering; Aeronautics; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003470324,0.0004183408,0.0001854557,0.0005086106,0.0001900139,0.0004797552,0.000117969,0.0002428945,0.0009035079],"category_scores_gemma":[0.00267964,0.0001256105,0.0001597642,0.0002164227,0.0001737806,0.0005620061,0.000379496,0.0002361671,0.0001279366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001556176,"about_ca_system_score_gemma":0.0001800996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00176639,"about_ca_topic_score_gemma":0.00174452,"domain_scores_codex":[0.9998662,0.00003209811,0.000009169088,0.00003249865,0.00003951054,0.00002055906],"domain_scores_gemma":[0.999454,0.0002424296,0.00008975482,0.00004110733,0.0001148029,0.00005787197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001490755,0.001028241,0.4511003,0.0003558374,0.000224046,0.0006236999,0.004770913,0.03386256,0.1377825,0.00136253,0.001518499,0.3658803],"study_design_scores_gemma":[0.0000419931,0.0008907092,0.6838351,0.00003730583,0.00007446555,0.0002565951,0.001265112,0.3007864,0.009916382,0.002164021,0.0006645478,0.00006737815],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791914,0.00004139499,0.01956651,0.00003859659,0.000009566719,0.00003402121,0.00005176407,0.00004771896,0.001019077],"genre_scores_gemma":[0.9972994,0.00002769241,0.002368178,0.000008757951,0.000004208965,0.00001685805,0.00004578841,0.000003670225,0.0002255036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00176639,"threshold_uncertainty_score":0.003512204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07020300513620087,"score_gpt":0.3780573735218215,"score_spread":0.3078543683856206,"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."}}