{"id":"W7065615220","doi":"","title":"Emotional turbulence during simulation training: Unraveling emotion dynamics and performance accuracy using simulations for pilot training","year":2024,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Silicon and Solar Cell Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Concordia University; Social Sciences and Humanities Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec; McGill University; U.S. Department of Transportation","keywords":"Dynamics (music); Training (meteorology); Turbulence; Range (aeronautics); Noise (video)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003933665,0.0007312537,0.0005814802,0.0007502029,0.001181741,0.0002140949,0.0003094164,0.0007209101,0.00002845751],"category_scores_gemma":[0.000752118,0.0008799668,0.0001975713,0.0005293507,0.00005568726,0.001032355,0.00006378096,0.001305982,0.00001175232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008643922,"about_ca_system_score_gemma":0.00005053393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001134306,"about_ca_topic_score_gemma":0.0001376724,"domain_scores_codex":[0.9971378,0.00004915865,0.0008693489,0.0008309105,0.0004296989,0.000683065],"domain_scores_gemma":[0.9985451,0.0004843084,0.0002412638,0.0003726359,0.0002070574,0.0001496353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001037543,0.00003577681,0.00004997367,0.002410317,0.0002215113,0.000008747246,0.000269715,0.7901084,0.04503001,0.008373353,1.429154e-7,0.1533882],"study_design_scores_gemma":[0.0006240149,0.0001069954,0.001799082,0.001327062,0.0001977562,0.00002456721,0.001614576,0.9792061,0.009191379,0.004840383,0.0001296056,0.000938505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938511,0.0003491799,0.000189719,0.000005073183,0.001387139,0.0008425256,0.0007017406,0.001435919,0.001237588],"genre_scores_gemma":[0.9946067,0.0002316986,0.003116412,0.00001196754,0.00009554498,0.00005977577,0.001168087,0.0002868872,0.0004228824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1890976,"threshold_uncertainty_score":0.9993651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04880258545455856,"score_gpt":0.2724902988446055,"score_spread":0.2236877133900469,"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."}}