{"id":"W2590967549","doi":"10.1155/2017/8652053","title":"Understanding Visitors’ Responses to Intelligent Transportation System in a Tourist City with a Mixed Ranked Logit Model","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Mixed logit; Tourism; Travel behavior; Logit; Preference; Public transport; Logistic regression; Ordered logit; Computer science; Transport engineering; Marketing; Operations research; Advertising; Econometrics; Geography; Business; Statistics; Engineering; Economics; Mathematics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001936701,0.0005603961,0.0003775927,0.0005656147,0.0002725358,0.001451023,0.0008458477,0.0007481506,0.003730212],"category_scores_gemma":[0.004696073,0.0002344434,0.001066973,0.0006052514,0.000342831,0.001015981,0.000748177,0.0006866656,0.0002408272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009284795,"about_ca_system_score_gemma":0.0006379967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008681245,"about_ca_topic_score_gemma":0.007147116,"domain_scores_codex":[0.9988002,0.0007826826,0.0000419544,0.0001710951,0.00006892163,0.0001352497],"domain_scores_gemma":[0.9977069,0.001756056,0.0002220063,0.00006676398,0.0001543237,0.00009381315],"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":[0.001361229,0.001293808,0.401417,0.0003775747,0.0008011407,0.000887783,0.003389202,0.4954065,0.005574096,0.03113353,0.002004235,0.05635381],"study_design_scores_gemma":[0.00002562127,0.0003681596,0.02578845,0.00001661299,0.0001128047,0.00004129883,0.001305961,0.9663027,0.0003007528,0.005236971,0.0004623568,0.00003835322],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546391,0.00006259887,0.04289081,0.0002757031,0.00001400592,0.00008801383,0.0002602507,0.00005731006,0.001712099],"genre_scores_gemma":[0.9955782,0.0000314702,0.003432167,0.00002370974,0.000004579152,0.00007804044,0.0001094628,0.00000359125,0.0007386498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008681245,"threshold_uncertainty_score":0.01726139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1765706331775326,"score_gpt":0.263888892184642,"score_spread":0.0873182590071094,"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."}}