{"id":"W2765513165","doi":"10.1155/2017/3192967","title":"Turnout Fault Diagnosis through Dynamic Time Warping and Signal Normalization","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"State Key Laboratory of Rail Traffic Control and Safety; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Turnout; Dynamic time warping; Normalization (sociology); Microcomputer; Fault (geology); Computer science; Similarity (geometry); Medical diagnosis; Image warping; Data mining; Artificial intelligence; Telecommunications; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003498791,0.0009261682,0.0004769958,0.001820777,0.0002819502,0.000683659,0.0004583566,0.0004153874,0.0009096799],"category_scores_gemma":[0.001888285,0.0002362426,0.0004324573,0.001205671,0.0004112873,0.001094642,0.0004861437,0.0004380688,0.0004160546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003289127,"about_ca_system_score_gemma":0.0004708679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002441598,"about_ca_topic_score_gemma":0.001669729,"domain_scores_codex":[0.9995159,0.00004969156,0.00003640932,0.0001702641,0.0001806513,0.00004702289],"domain_scores_gemma":[0.999453,0.0001413738,0.0001183481,0.00007211968,0.000184472,0.00003080539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003984713,0.00007457103,0.005804082,0.0001314835,0.0000491571,0.0005117041,0.0002739606,0.0505483,0.1150046,0.001964067,0.001205611,0.8240341],"study_design_scores_gemma":[0.00001727088,0.0001896496,0.01053288,0.00001961756,0.00005971334,0.0007835182,0.0001982804,0.8805323,0.1012048,0.002270231,0.004145027,0.00004674928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09937052,0.000314625,0.8967848,0.00009654477,0.0000630286,0.00007136193,0.00008781644,0.001685091,0.001526312],"genre_scores_gemma":[0.7575304,0.0003974508,0.2394079,0.00004497456,0.00003460904,0.00006653849,0.000297,0.0001304374,0.002090768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002441598,"threshold_uncertainty_score":0.004854739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004544930960011549,"score_gpt":0.2206218313178903,"score_spread":0.2160769003578788,"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."}}