{"id":"W3093106141","doi":"10.1155/2020/1796132","title":"Evaluating Railway Operation Safety Situation in China Based on an Improved TOPSIS Method: A Regional Perspective","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"TOPSIS; Ideal solution; Computer science; Cosine similarity; Quality (philosophy); Evaluation methods; Operations research; Transport engineering; Measure (data warehouse); Risk analysis (engineering); Reliability engineering; Data mining; Engineering; Cluster analysis; Artificial intelligence; Business","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":[0.005259264,0.0002000688,0.0004698237,0.0005623347,0.0001363449,0.0001742811,0.0003909466,0.00009463513,0.0002546741],"category_scores_gemma":[0.004022157,0.0001624236,0.0002008334,0.000979544,0.00002611176,0.002322997,0.000004234819,0.0003831238,0.000007158516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002467284,"about_ca_system_score_gemma":0.000255867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002247215,"about_ca_topic_score_gemma":0.0001667876,"domain_scores_codex":[0.9946642,0.0007701396,0.001796644,0.0005253455,0.002057282,0.0001863712],"domain_scores_gemma":[0.9958472,0.001162266,0.001247885,0.0002498658,0.001309892,0.0001829028],"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":[0.002811826,0.0001006677,0.0004336111,0.000004014862,0.000007427111,0.00001577013,0.01333085,0.7996143,0.07191476,0.000331295,0.00001021235,0.1114253],"study_design_scores_gemma":[0.003981125,0.001235118,0.2875435,0.00008420771,0.00002780672,0.000004432493,0.007677388,0.692355,0.001718005,0.005046586,0.00013003,0.0001967668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4970688,0.0000272763,0.4982194,0.004078283,0.0002264809,0.0003021483,0.00001162315,0.00001337544,0.0000526332],"genre_scores_gemma":[0.7459441,0.000005687703,0.2531765,0.000677905,0.0001502903,0.000005574572,0.00001840968,0.00001518728,0.000006302183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2871099,"threshold_uncertainty_score":0.6623443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1572751389710265,"score_gpt":0.4841794105772099,"score_spread":0.3269042716061833,"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."}}