{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008589873,0.0001603076,0.000263893,0.0002363234,0.0001471507,0.0000157324,0.0001980563,0.00005476352,0.000180717],"category_scores_gemma":[0.0002033815,0.000187809,0.00008778402,0.0009314867,0.0001311652,0.000196448,0.0001010824,0.0003229307,3.145578e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001479121,"about_ca_system_score_gemma":0.0004336191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00262876,"about_ca_topic_score_gemma":0.001003928,"domain_scores_codex":[0.9984276,0.0001023518,0.0005795172,0.0003041055,0.0001797393,0.0004067416],"domain_scores_gemma":[0.9989394,0.00008051577,0.00005530144,0.0004587737,0.0004031732,0.00006286032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00007952684,0.0009912105,0.3012155,0.003266944,0.00005705849,0.000891786,0.01921381,0.6471971,0.0001600583,0.0212715,0.00006844899,0.005587019],"study_design_scores_gemma":[0.001728828,0.0003623643,0.70885,0.00001286625,0.00005843425,0.0002135399,0.203805,0.03411739,0.00263604,0.04384613,0.003425381,0.0009440632],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973535,0.00009756872,0.000745701,0.0002145235,0.00007798957,0.0009069345,0.00007459349,0.0002112641,0.0003179451],"genre_scores_gemma":[0.9992348,0.000001835494,0.0001441626,0.00001927927,0.000006674127,0.0004828006,0.00002836082,0.00001611934,0.00006596346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6130797,"threshold_uncertainty_score":0.7658627,"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."}}