{"id":"W2070583489","doi":"10.1016/j.cor.2013.06.013","title":"Rapid transit network design for optimal cost and origin–destination demand capture","year":2013,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Computer science; Mathematical optimization; Graph; Vertex (graph theory); Transit (satellite); Network planning and design; Transit time; Integer (computer science); Travel time; Integer programming; Transit system; Graph theory; Operations research; Algorithm; Public transport; Theoretical computer science; Mathematics; Transport engineering; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001463059,0.00008009359,0.00009980256,0.0001240443,0.00180657,0.0005279378,0.0001458463,0.00009605653,0.00008934159],"category_scores_gemma":[0.0001156309,0.00007965889,0.00002393388,0.0004378754,0.0002066507,0.0004618162,0.000007593386,0.000156101,0.00001652104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005603522,"about_ca_system_score_gemma":0.000238696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008719811,"about_ca_topic_score_gemma":0.0005290279,"domain_scores_codex":[0.9985251,0.0003884721,0.0001677861,0.0002359139,0.0003198569,0.0003628833],"domain_scores_gemma":[0.9984949,0.0005332519,0.00001468206,0.00009264784,0.0007145205,0.0001500018],"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":[0.00001436774,0.00002322666,0.0002027871,0.000008356962,0.00001084099,7.857259e-7,0.00911413,0.9582456,0.00003298395,0.009654637,0.01127748,0.01141482],"study_design_scores_gemma":[0.0005936447,0.000111938,0.002748895,0.00004080355,0.00001153833,0.000001336705,0.002070671,0.9735286,0.00003188885,0.0002558189,0.02043999,0.0001648696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01628588,0.0002357511,0.9782224,0.002846232,0.0001614043,0.001773287,0.000006941193,0.00006934955,0.0003987462],"genre_scores_gemma":[0.7059882,0.0002066862,0.2917795,0.0001364586,0.0003342482,0.0004310979,0.0001503381,0.00001530023,0.0009581551],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6897023,"threshold_uncertainty_score":0.9994929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1027564940145313,"score_gpt":0.3781683368488936,"score_spread":0.2754118428343624,"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."}}