{"id":"W4296782845","doi":"10.1155/2022/5830261","title":"Urban Residents’ Willingness to Choose and Pay for ADAS and Autonomous Driving Functions: Comparison of Two Cities in China","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Willingness to pay; China; Profitability index; Business; Population; Logit; Marketing; Mixed logit; Logistic regression; Economics; Geography; Environmental health; Computer science; Econometrics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002630688,0.0000755575,0.0002260639,0.0002449724,0.0001161447,0.00001076557,0.00004985371,0.00002084814,0.0001572052],"category_scores_gemma":[0.00002319658,0.0000717925,0.0000457313,0.0001283678,0.00001705184,0.0002414544,0.000002586837,0.0001878507,3.265384e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005265478,"about_ca_system_score_gemma":0.00002496438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004463638,"about_ca_topic_score_gemma":0.0002632194,"domain_scores_codex":[0.9989386,0.00005791138,0.0006555262,0.0001135194,0.0001430111,0.00009141307],"domain_scores_gemma":[0.9992505,0.0001130598,0.0004375454,0.00006015505,0.00008948492,0.00004927949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001845566,0.0003532462,0.7384539,0.00009394602,0.00009759863,0.00001261251,0.124869,0.09218594,0.002612353,0.003176657,0.0009368657,0.0353624],"study_design_scores_gemma":[0.001944251,0.0003713924,0.9800864,0.00005346306,0.00002662403,0.00001227638,0.01341132,0.0001863932,0.000105216,0.0004594046,0.003267777,0.00007546962],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901038,0.0002313092,0.008260919,0.0003359454,0.0007647873,0.0002074599,0.00001972277,0.00001017208,0.00006590621],"genre_scores_gemma":[0.9980865,0.00001320745,0.001370371,0.00004862597,0.00004947702,0.0000371727,0.0000153468,0.00001051615,0.0003687715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2416326,"threshold_uncertainty_score":0.2927613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01822328701410269,"score_gpt":0.3551737528629808,"score_spread":0.3369504658488781,"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."}}