{"id":"W4220809865","doi":"10.1155/2022/8595356","title":"Resilience-Based Restoration Sequence Optimization for Metro Networks: A Case Study in China","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Government of Jiangsu Province","keywords":"Resilience (materials science); Sequence (biology); Computer science; Node (physics); Operations research; Transfer (computing); Reliability engineering; Sensitivity (control systems); Engineering; Structural engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004740981,0.00009778548,0.0001986115,0.0003104331,0.0001401052,0.00001492207,0.00009320895,0.00002646476,0.00002485324],"category_scores_gemma":[0.00003132775,0.0001001357,0.00008357607,0.0007079348,0.00001381073,0.0004753268,0.000001003167,0.0002377557,2.9105e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002480978,"about_ca_system_score_gemma":0.00005562521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002951794,"about_ca_topic_score_gemma":0.0006469371,"domain_scores_codex":[0.9988664,0.00007100218,0.0005660367,0.0001139648,0.0002522341,0.0001303558],"domain_scores_gemma":[0.9994947,0.00006649306,0.0001974844,0.00009833556,0.0001070415,0.00003597264],"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":[0.0001594782,0.00006947096,0.007591349,0.00002259277,0.00001532654,0.0002162176,0.001243346,0.9872565,0.0002873827,0.000007570514,0.000004768243,0.003126028],"study_design_scores_gemma":[0.00130146,0.0005730088,0.01891513,0.00001447675,0.00007209901,0.00004004202,0.006821968,0.971881,0.0001392424,0.00009888646,0.00002156703,0.0001211596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.617575,0.00006281827,0.3819571,0.00001806114,0.0001430285,0.0002236678,0.000006711688,0.00001201057,0.000001577375],"genre_scores_gemma":[0.983155,0.00001215393,0.01668324,0.00001132497,0.00004165628,0.00005196328,0.00003115259,0.00001256221,9.935195e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3655799,"threshold_uncertainty_score":0.4083413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009655012342940437,"score_gpt":0.2627904976524206,"score_spread":0.2531354853094802,"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."}}