{"id":"W4313225615","doi":"10.3390/su15010460","title":"Moving toward a More Sustainable Autonomous Mobility, Case of Heterogeneity in Preferences","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"University Canada West","keywords":"Taxis; Preference; Modal shift; Mixed logit; Sustainable transport; Travel behavior; Modal; Logit; Business; Revealed preference; Computer science; Transport engineering; Marketing; Environmental economics; Econometrics; Logistic regression; Economics; Sustainability; Microeconomics; Public transport; Engineering; Ecology","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":[0.003553551,0.0003944199,0.0007380969,0.0008666401,0.0009094788,0.00197576,0.0008717236,0.001237278,0.006533054],"category_scores_gemma":[0.01007732,0.0002741354,0.001249386,0.001269569,0.001036955,0.001766691,0.001292385,0.001329469,0.000396445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00119205,"about_ca_system_score_gemma":0.0006710418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005950871,"about_ca_topic_score_gemma":0.006245345,"domain_scores_codex":[0.9967481,0.001567359,0.0001845801,0.0005895104,0.0002942137,0.000616256],"domain_scores_gemma":[0.9926074,0.003807098,0.00196422,0.0009634566,0.000389195,0.0002687318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008182263,0.000811121,0.6875842,0.0006288528,0.001125254,0.01050718,0.005114052,0.06097848,0.003869638,0.1556264,0.003533693,0.06940298],"study_design_scores_gemma":[0.0002935321,0.001061191,0.4146561,0.0003119577,0.0006656171,0.005534115,0.02825343,0.2661399,0.002735097,0.266556,0.01351667,0.0002762994],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9406114,0.0003070786,0.04654649,0.002231892,0.00004238277,0.0001363064,0.0005256116,0.00002796215,0.009570776],"genre_scores_gemma":[0.9975075,0.00004914585,0.001344676,0.00006544546,0.00001352149,0.00003644708,0.00007506213,0.000001776174,0.0009065061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006533054,"threshold_uncertainty_score":0.02185524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01387183587673322,"score_gpt":0.2685293153857197,"score_spread":0.2546574795089865,"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."}}