{"id":"W2518271336","doi":"10.1016/j.cor.2016.08.009","title":"Multi-objective rapid transit network design with modal competition: The case of Concepción, Chile","year":2016,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Natural Sciences and Engineering Research Council of Canada; Comisión Nacional de Investigación Científica y Tecnológica; Instituto de Sistemas Complejos de Ingeniería; Consiglio Nazionale delle Ricerche","keywords":"Modal; Competition (biology); Computer science; Integer (computer science); Mathematical optimization; Network planning and design; Integer programming; Transit (satellite); Mathematics; Transport engineering; Engineering; Public transport; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001484795,0.001181234,0.001263536,0.001107784,0.001100003,0.001912902,0.001541623,0.002170026,0.004358075],"category_scores_gemma":[0.002234248,0.0007478907,0.001129294,0.0009704479,0.0009281469,0.001119468,0.001045763,0.0009132256,0.0001859929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003237049,"about_ca_system_score_gemma":0.002727998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06395126,"about_ca_topic_score_gemma":0.04955742,"domain_scores_codex":[0.9995342,0.0002314043,0.000009146947,0.00005529646,0.00004231514,0.0001276477],"domain_scores_gemma":[0.9988822,0.0007161531,0.00009647376,0.00002424065,0.0001578227,0.0001231564],"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":[0.00005247505,0.00004189816,0.0003770954,0.00004537808,0.00001726746,0.0002209463,0.00002693156,0.9955614,0.000254644,0.002038468,0.0002609842,0.001102498],"study_design_scores_gemma":[0.00003304065,0.00007128379,0.0004219059,0.000009199081,0.00001885884,0.0000265786,0.0001318993,0.997611,0.0001319883,0.001220098,0.0003138513,0.00001025069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.850176,0.0007124717,0.1011221,0.001155772,0.00008407719,0.0003430798,0.0004931405,0.0001506995,0.04576259],"genre_scores_gemma":[0.9847254,0.0001393133,0.007385954,0.00003378556,0.00001300728,0.0001042892,0.00007472956,0.00002557862,0.007497849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06395126,"threshold_uncertainty_score":0.127158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07772687742908109,"score_gpt":0.36079891512287,"score_spread":0.283072037693789,"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."}}