{"id":"W4282596060","doi":"10.1111/poms.13775","title":"Smart urban transport and logistics: A business analytics perspective","year":2022,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; Ministry of Education - Singapore","keywords":"Perspective (graphical); Computer science; Big data; Analytics; Sustainability; Business analytics; Software; Process management; Data analysis; Data science; Engineering management; Business model; Knowledge management; Business; Business analysis; Marketing; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002910736,0.001140052,0.0006436381,0.007778144,0.001037046,0.01322647,0.00120861,0.002630909,0.002425754],"category_scores_gemma":[0.003467251,0.0004540832,0.000583628,0.01064378,0.00437589,0.01460688,0.002487323,0.003471461,0.0006836581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003271472,"about_ca_system_score_gemma":0.00296196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003241089,"about_ca_topic_score_gemma":0.002455213,"domain_scores_codex":[0.9978889,0.0009937566,0.0001211129,0.0001965796,0.000633299,0.0001664099],"domain_scores_gemma":[0.9956067,0.003193075,0.0002857864,0.0001873827,0.0004520851,0.0002749978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002020898,0.00008352561,0.0024502,0.0006823188,0.00004822019,0.0002694232,0.0009558994,0.00580853,0.0004420494,0.9052408,0.009894446,0.07410426],"study_design_scores_gemma":[0.000009233098,0.00005986504,0.00198619,0.001478328,0.0000387015,0.000442977,0.006097744,0.04029218,0.001359606,0.663526,0.2846439,0.00006538665],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03334788,0.1902132,0.3695934,0.1894291,0.001884743,0.000224462,0.001329559,0.0006822486,0.2132955],"genre_scores_gemma":[0.5864854,0.2282446,0.1555133,0.009920138,0.005458059,0.0002512592,0.001268641,0.0001610428,0.01269755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01322647,"threshold_uncertainty_score":0.0237363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314102065826254,"score_gpt":0.2176477012483222,"score_spread":0.2045066805900597,"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."}}