{"id":"W7133024743","doi":"","title":"Leveraging Cellular and Bluetooth Sensor Data for Enhanced Urban Travel Time Predictions","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Flexibility (engineering); Bluetooth; Global Positioning System; Context (archaeology); Software deployment; Smart city; Cellular network; Domain (mathematical analysis); Perspective (graphical)","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":[0.0004156774,0.000708395,0.0003659682,0.001057947,0.0002020368,0.0009847038,0.0009358988,0.0006504175,0.0006579392],"category_scores_gemma":[0.003131469,0.0003147229,0.0004636463,0.001504508,0.0002528059,0.00155782,0.000803035,0.0009526341,0.0004307761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005225746,"about_ca_system_score_gemma":0.0004948989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01165151,"about_ca_topic_score_gemma":0.01756573,"domain_scores_codex":[0.9997533,0.00004921274,0.0000141389,0.00008367123,0.00006546419,0.00003417972],"domain_scores_gemma":[0.9990705,0.0004057901,0.0001424226,0.000155014,0.0001689576,0.00005736576],"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":[0.00007247781,0.00008594053,0.02703832,0.00005266978,0.00006068283,0.0001031563,0.0001059206,0.909767,0.001487381,0.003863055,0.0015926,0.05577086],"study_design_scores_gemma":[0.000001310947,0.000006823691,0.001520695,0.000004613557,0.000005746305,0.00001500627,0.00002009155,0.9963362,0.0003199112,0.001362415,0.000401755,0.000005331397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3779537,0.0005507183,0.6095952,0.001044584,0.0002044149,0.00007678018,0.002670463,0.001702839,0.006201347],"genre_scores_gemma":[0.9687151,0.0002395572,0.02872608,0.00005936586,0.00005465577,0.00003654308,0.001337328,0.00004338604,0.0007879143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01165151,"threshold_uncertainty_score":0.02316737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02899743792650675,"score_gpt":0.2909593696262329,"score_spread":0.2619619316997261,"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."}}