{"id":"W4287662306","doi":"10.48550/arxiv.2009.12197","title":"End-to-End Prediction of Parcel Delivery Time with Deep Learning for\\n Smart-City Applications","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Deep learning; Convolutional neural network; Artificial intelligence; Cloud computing; Machine learning; Last mile (transportation); Predictability; Architecture; Artificial neural network; Big data; Data science; Data mining; Mile","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005400322,0.001226453,0.0005908858,0.0005307143,0.0003436451,0.0006722599,0.00144133,0.0008918335,0.002097772],"category_scores_gemma":[0.001591476,0.0004037952,0.0004922919,0.000810884,0.0003675931,0.001196838,0.0007273375,0.001590083,0.0008634881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001250757,"about_ca_system_score_gemma":0.00128401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04656627,"about_ca_topic_score_gemma":0.0678914,"domain_scores_codex":[0.9997924,0.00002902771,0.00001013738,0.00008180005,0.00003069779,0.00005595226],"domain_scores_gemma":[0.999642,0.0001371212,0.0000405635,0.00005913493,0.00008165331,0.00003959258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004142208,0.0004676363,0.008816252,0.00009553541,0.0001101392,0.0001424837,0.00006583992,0.8249174,0.003086276,0.001338683,0.01358587,0.1469597],"study_design_scores_gemma":[0.000004514148,0.00001012862,0.0003604416,0.000002225556,0.000002545199,0.000002916043,0.000009626536,0.9984432,0.0005144801,0.0004166717,0.0002311105,0.000002212647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6618579,0.002100933,0.304287,0.001833061,0.0003353112,0.0001549754,0.005834645,0.01704337,0.006552733],"genre_scores_gemma":[0.9214079,0.0003952188,0.06215951,0.0003069646,0.00007130225,0.00008782496,0.009671255,0.000208443,0.005691639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04656627,"threshold_uncertainty_score":0.09259045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03248264046081951,"score_gpt":0.1607235054509683,"score_spread":0.1282408649901488,"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."}}