{"id":"W2800265124","doi":"10.1155/2018/1329265","title":"Cluster Analysis Based Arc Detection in Pantograph-Catenary System","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Electrical Contact Performance and Analysis","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Pantograph; Catenary; Computer science; Frame (networking); Arc (geometry); Segmentation; Interference (communication); Electric arc; Cluster (spacecraft); Simulation; Real-time computing; Artificial intelligence; Engineering; Engineering drawing; Telecommunications; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002757382,0.0005385738,0.0005566839,0.002125805,0.0004707429,0.0005045658,0.0005445355,0.0003957704,0.0008547019],"category_scores_gemma":[0.0009182667,0.0001887728,0.0003745343,0.001781453,0.0002654129,0.000475214,0.0004084751,0.0003066872,0.0002481598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005787059,"about_ca_system_score_gemma":0.000585206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01447199,"about_ca_topic_score_gemma":0.008595041,"domain_scores_codex":[0.9995136,0.00005339632,0.00002774043,0.000152578,0.0001850358,0.00006762743],"domain_scores_gemma":[0.9996382,0.00007867963,0.00005455902,0.00002622738,0.0001786758,0.00002364618],"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":[0.0008573001,0.0002633091,0.0269138,0.0003517416,0.0001711333,0.0008257156,0.0008582376,0.1968145,0.1131166,0.003506177,0.005904797,0.6504167],"study_design_scores_gemma":[0.00001332697,0.00009829956,0.01881222,0.000007610558,0.00003608388,0.0001918416,0.0002710806,0.9604977,0.01773273,0.0007141475,0.001585164,0.00003983998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.345878,0.000298392,0.6463911,0.0001382693,0.00006888491,0.0001684431,0.0003467606,0.002951167,0.003758998],"genre_scores_gemma":[0.9084125,0.0001531883,0.08899166,0.00001922149,0.00001658515,0.00006917852,0.0003332369,0.00006121079,0.001943285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01447199,"threshold_uncertainty_score":0.02877545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004150278746274213,"score_gpt":0.2068636012440281,"score_spread":0.2027133224977538,"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."}}