{"id":"W2070504508","doi":"10.3141/2138-13","title":"Methodology to Analyze Adaptation in Driving Simulators","year":2009,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; University of British Columbia","funders":"","keywords":"Adaptation (eye); Driving simulator; Distraction; Computer science; Task (project management); Learning curve; Variety (cybernetics); Simulation; Engineering; Artificial intelligence; Psychology; Cognitive psychology","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":[0.01976145,0.001474096,0.0009100474,0.004649687,0.0009639675,0.001810934,0.001966246,0.001104115,0.009962863],"category_scores_gemma":[0.05173443,0.0007298221,0.00138263,0.002637858,0.001299679,0.00112015,0.002679789,0.001502837,0.002198796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001217764,"about_ca_system_score_gemma":0.003266685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001069072,"about_ca_topic_score_gemma":0.001230903,"domain_scores_codex":[0.9795153,0.009719092,0.002668158,0.002674048,0.004765006,0.0006584119],"domain_scores_gemma":[0.9592556,0.01857463,0.005112888,0.005376314,0.01115085,0.0005297242],"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":[0.00124675,0.002277415,0.04413635,0.00534243,0.0003786408,0.0005831187,0.0154384,0.006138283,0.04837565,0.03753906,0.005991408,0.8325526],"study_design_scores_gemma":[0.001621152,0.02609474,0.2588552,0.003814821,0.0008821577,0.002786092,0.03596464,0.08255269,0.1473231,0.09476037,0.3443503,0.0009946717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04391405,0.0002037894,0.9163573,0.0001592935,0.0001414392,0.03106272,0.00129118,0.0008945457,0.00597565],"genre_scores_gemma":[0.07009454,0.0001771114,0.8520266,0.0001717758,0.00003651347,0.07435422,0.0008024045,0.0001463468,0.002190481],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01976145,"threshold_uncertainty_score":0.1045098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2278907952605116,"score_gpt":0.5129330101156925,"score_spread":0.2850422148551809,"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."}}