{"id":"W4306295129","doi":"10.48550/arxiv.2210.06683","title":"Augmenting Flight Training with AI to Efficiently Train Pilots","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Trainer; Formative assessment; Computer science; Focus (optics); Flight training; Aeronautics; Training (meteorology); Human–computer interaction; Multimedia; Simulation; Flight simulator; Artificial intelligence; Engineering; Mathematics education; Psychology; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007606152,0.00061521,0.0002941548,0.0003709824,0.0002727484,0.0005972949,0.0009441652,0.0007004581,0.003269787],"category_scores_gemma":[0.003970107,0.0003514215,0.0001924606,0.0001850946,0.0004841561,0.0009880903,0.0009975091,0.0008478371,0.0008959557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003397052,"about_ca_system_score_gemma":0.0007976499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002474347,"about_ca_topic_score_gemma":0.003106915,"domain_scores_codex":[0.9995098,0.000195488,0.00002253297,0.0001116614,0.0001084933,0.00005191176],"domain_scores_gemma":[0.9985167,0.0007943084,0.000125639,0.0002283634,0.000230059,0.0001047695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003745708,0.001323584,0.00580828,0.0003107297,0.00004855474,0.0002596479,0.001481212,0.2286548,0.09539212,0.007179362,0.003641979,0.6555251],"study_design_scores_gemma":[0.00007017137,0.0006159649,0.002133339,0.00004729859,0.00003457978,0.0001497673,0.0002196986,0.9334779,0.04091934,0.006351912,0.0159482,0.00003177095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1635311,0.0001561167,0.816286,0.0006628384,0.00005960394,0.0003081521,0.00006951566,0.006913822,0.01201298],"genre_scores_gemma":[0.6197932,0.0001211417,0.3761353,0.0001243231,0.00002235346,0.0001808944,0.000102451,0.000107551,0.003412709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003269787,"threshold_uncertainty_score":0.01093853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08459200535628124,"score_gpt":0.1913075919923399,"score_spread":0.1067155866360587,"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."}}