{"id":"W4312884605","doi":"10.4236/jbbs.2022.1210027","title":"Cognitive Workload Assessment of Aircraft Pilots","year":2022,"lang":"en","type":"article","venue":"Journal of Behavioral and Brain Science","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Takeoff; Workload; Pupillary response; Cognition; Airplane; Pupillometry; Simulation; Cognitive load; Computer science; Pupil; Aeronautics; Moment (physics); Psychology; Engineering; Automotive engineering; Aerospace engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002845665,0.0005966389,0.0002495507,0.0004043681,0.0001776953,0.0004181351,0.0001831496,0.0003501899,0.001034222],"category_scores_gemma":[0.00223409,0.0001127434,0.00017194,0.0001335701,0.0001181378,0.0003011758,0.0003240059,0.0003194663,0.0002394285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001461386,"about_ca_system_score_gemma":0.000193997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001520498,"about_ca_topic_score_gemma":0.002089389,"domain_scores_codex":[0.9998091,0.00004629818,0.00001396901,0.00004609696,0.00005439756,0.00003023513],"domain_scores_gemma":[0.9994619,0.0001838374,0.0001115501,0.00002853757,0.0001172054,0.00009698245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003189222,0.001518808,0.4437464,0.0006176436,0.0003476018,0.0008726653,0.003250562,0.02914326,0.27664,0.0004759235,0.00207646,0.2381215],"study_design_scores_gemma":[0.00006458571,0.003648394,0.8460698,0.00006164181,0.00009632526,0.0005436642,0.00125879,0.1279059,0.01813898,0.0007902536,0.001330443,0.00009113067],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900751,0.00008149417,0.00898685,0.00004621511,0.00001946682,0.00003514211,0.0001493545,0.00005266533,0.0005537374],"genre_scores_gemma":[0.997272,0.00004966457,0.002246846,0.00002400763,0.00001690189,0.00002843222,0.0001492665,0.00000420913,0.000208781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001520498,"threshold_uncertainty_score":0.003459871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0667017914974978,"score_gpt":0.4583426932574531,"score_spread":0.3916409017599553,"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."}}