{"id":"W4391768829","doi":"10.1109/itsc57777.2023.10422363","title":"Augmenting Transit Network Design Algorithms with Deep Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Deep learning; Transit (satellite); Artificial intelligence; Algorithm; Machine learning; Public transport; Engineering; Transport engineering","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":[0.0008999874,0.001471515,0.0008193709,0.0009109995,0.0003413432,0.0009117788,0.001159797,0.001223515,0.004027421],"category_scores_gemma":[0.004064374,0.0007945449,0.0006127815,0.0008306013,0.0005981962,0.001593682,0.001064297,0.001905337,0.0008027289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001276168,"about_ca_system_score_gemma":0.0013621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01046242,"about_ca_topic_score_gemma":0.01768022,"domain_scores_codex":[0.9996462,0.0001049887,0.0000192769,0.00008427334,0.00009322116,0.00005202015],"domain_scores_gemma":[0.9984431,0.0009876721,0.0001368531,0.000161221,0.0002269945,0.00004421539],"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":[0.00002767084,0.00004680938,0.0003532357,0.00003835445,0.00002041612,0.00001455762,0.00001133536,0.9371825,0.0005387699,0.003010111,0.000851399,0.05790493],"study_design_scores_gemma":[0.000003155574,0.000006611203,0.00001762132,0.000002689075,0.000002279276,0.000002261637,0.000001600104,0.9975498,0.0001951789,0.001956574,0.0002611837,0.000001005636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01832978,0.0004139816,0.9756031,0.000331867,0.00005571864,0.0000431826,0.0001037221,0.001431262,0.003687404],"genre_scores_gemma":[0.5173546,0.0005004991,0.4751891,0.0004162588,0.00008620499,0.0001622684,0.0006325885,0.0003190134,0.005339465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01046242,"threshold_uncertainty_score":0.02080309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01208942323122711,"score_gpt":0.1933537615148309,"score_spread":0.1812643382836038,"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."}}