{"id":"W2889357795","doi":"10.1155/2018/3985302","title":"Analyzing Capacity Utilization and Travel Patterns of Chinese High-Speed Trains: An Exploratory Data Mining Approach","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Train; Beijing; Cluster analysis; Principal component analysis; Computer science; Principal (computer security); Transport engineering; Operations research; Engineering; Data mining; Artificial intelligence; China","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":[0.0002633067,0.0001182806,0.0002486313,0.0001451244,0.00004454329,0.00001314246,0.0001438108,0.00005218776,0.000002803106],"category_scores_gemma":[0.00001215532,0.0001029463,0.00002979509,0.0001879316,0.00003403936,0.001082834,0.000001614247,0.00008268344,7.430436e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001548309,"about_ca_system_score_gemma":0.00001494644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001483843,"about_ca_topic_score_gemma":0.0001439476,"domain_scores_codex":[0.9990265,0.0000240638,0.0005352961,0.0001297425,0.0001749571,0.0001094018],"domain_scores_gemma":[0.9993685,0.0000156014,0.0002283828,0.0001739788,0.0001427097,0.0000708072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001048105,0.0001740095,0.03126808,0.0005235135,0.0001353989,0.000009770613,0.04625155,0.7238358,0.1709523,0.0002089696,0.000005503657,0.02653036],"study_design_scores_gemma":[0.001632938,0.0003828662,0.8568844,0.0002362271,0.00008554427,0.00002122417,0.008779412,0.1255948,0.005956039,0.00008740088,0.00004460023,0.0002946082],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8255526,0.0001834609,0.1739178,0.000002217924,0.0002217427,0.00004367693,0.00003775349,0.00001596119,0.00002475081],"genre_scores_gemma":[0.988261,0.0001389998,0.01130142,0.000002485298,0.0001666981,7.374337e-7,0.0001068099,0.00002005148,0.000001787358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8256163,"threshold_uncertainty_score":0.419803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04082400270061062,"score_gpt":0.2572501141278168,"score_spread":0.2164261114272062,"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."}}