{"id":"W2953609581","doi":"10.1155/2019/3689389","title":"Analyses of the Imbalance of Urban Taxis’ High-Quality Customers Based on Didi Trajectory Data","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Social Science Fund of China; National Natural Science Foundation of China","keywords":"Taxis; Distribution (mathematics); Revenue; Order (exchange); Business; Transport engineering; Quality (philosophy); Dimension (graph theory); Computer science; Operations research; Engineering; Mathematics","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":[0.001066638,0.000468435,0.000492279,0.002999807,0.0003767794,0.000770726,0.0005507623,0.0004130168,0.00128898],"category_scores_gemma":[0.002965222,0.000218429,0.0005615237,0.004862673,0.0002596762,0.0006499704,0.0007156442,0.000519784,0.0005054864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001117849,"about_ca_system_score_gemma":0.000537574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04630353,"about_ca_topic_score_gemma":0.03379615,"domain_scores_codex":[0.9992465,0.0001287843,0.00006061134,0.000172208,0.0002028752,0.0001890491],"domain_scores_gemma":[0.9978969,0.00047062,0.0004616618,0.0002040064,0.0007706733,0.0001960727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002252818,0.0001207354,0.9661718,0.00005167659,0.0001557064,0.0002571236,0.0004136697,0.01961256,0.0009803132,0.0006665901,0.001517559,0.009826996],"study_design_scores_gemma":[0.000009707771,0.00003774511,0.9028525,0.00001458329,0.00004532524,0.00009015342,0.001316164,0.09292635,0.0005744694,0.0002571308,0.001851136,0.00002471922],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926024,0.00006917318,0.001130549,0.00007578171,0.000006825729,0.00001815872,0.005304398,0.00005424114,0.0007383255],"genre_scores_gemma":[0.9896713,0.00004530492,0.000597182,0.000006878385,0.00000576758,0.00001571096,0.009332994,0.00001074895,0.0003139825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04630353,"threshold_uncertainty_score":0.09206808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02930693874964392,"score_gpt":0.3056825048508582,"score_spread":0.2763755661012143,"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."}}