{"id":"W2772756146","doi":"10.1155/2017/5656323","title":"A Gradual Approach for Multimodel Journey Planning: A Case Study in Izmir, Turkey","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Dijkstra's algorithm; Public transport; Computer science; Router; Graph; Plan (archaeology); Transportation planning; Route planning; Operations research; Motion planning; Routing (electronic design automation); Node (physics); Transport engineering; Routing algorithm; Shortest path problem; Engineering; Robot; Artificial intelligence; Computer network; Routing protocol","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":[0.0007838036,0.0005664571,0.0003226136,0.000692384,0.001874896,0.0009504809,0.001388599,0.001452662,0.00288267],"category_scores_gemma":[0.001268242,0.000237638,0.0007132772,0.001438864,0.0006539137,0.001263527,0.000848059,0.0007261654,0.0003167206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002655132,"about_ca_system_score_gemma":0.001838619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03542692,"about_ca_topic_score_gemma":0.06071165,"domain_scores_codex":[0.9995205,0.0002054202,0.00002475596,0.00008746502,0.00004686472,0.0001150129],"domain_scores_gemma":[0.9994362,0.0002305622,0.00004841131,0.00007022695,0.00009372005,0.0001208806],"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":[0.001079644,0.001766924,0.03849552,0.0008175054,0.0001315147,0.02426584,0.007119561,0.7463528,0.008206654,0.03280466,0.008075856,0.1308836],"study_design_scores_gemma":[0.0002526349,0.001052109,0.03531637,0.0001230086,0.0001661507,0.003315445,0.02969903,0.865514,0.00880492,0.01008115,0.04549479,0.0001804495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9629979,0.0001908378,0.02754802,0.0004115651,0.00002804274,0.0002314495,0.000356594,0.0002688918,0.007966754],"genre_scores_gemma":[0.9598036,0.0001423252,0.03711721,0.00002526001,0.000003701676,0.00007839905,0.0003249814,0.00003880258,0.002465679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03542692,"threshold_uncertainty_score":0.07044142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06903255165066252,"score_gpt":0.3468965048434949,"score_spread":0.2778639531928324,"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."}}