{"id":"W2099349620","doi":"10.1145/1081870.1081929","title":"Learning to predict train wheel failures","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Prognostics; Train; Computer science; Feature extraction; Data modeling; Feature (linguistics); Engineering; Machine learning; Artificial intelligence; Data mining; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000135586,0.00005550959,0.00005173565,0.00004419292,0.00009929806,0.000147305,0.0005446011,0.00001767625,0.00005723468],"category_scores_gemma":[0.0000234849,0.00004798159,0.00001845321,0.0002109684,0.000008766317,0.0002908865,0.0001460036,0.00007077835,0.0005039866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001149406,"about_ca_system_score_gemma":0.00001810207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003254189,"about_ca_topic_score_gemma":0.00002391404,"domain_scores_codex":[0.9994071,0.00001129726,0.00009176339,0.0002228428,0.0001144989,0.0001525479],"domain_scores_gemma":[0.9995613,0.00002806933,0.00001523148,0.0002813761,0.00001965892,0.00009432682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[2.302426e-7,0.00002194076,0.00005398782,7.375485e-7,0.000002878448,5.026802e-7,0.0008315234,0.0006644822,0.0003737237,0.03284165,0.02983852,0.9353698],"study_design_scores_gemma":[0.00006987373,0.00004391659,0.001054492,0.000003709653,9.728927e-7,0.000004176125,0.00008232841,0.2578111,0.0005419694,0.0001331519,0.7401678,0.00008646848],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003783529,0.0000100169,0.9664845,0.01454077,0.0000311402,0.00007609941,0.000002665226,0.0003365127,0.0147347],"genre_scores_gemma":[0.2163177,0.00000195219,0.7750439,0.0009132471,0.0001360955,0.00003115743,0.000004106365,0.000004614615,0.007547191],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9352834,"threshold_uncertainty_score":0.6477895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103356435586418,"score_gpt":0.2479855414430302,"score_spread":0.236951977087166,"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."}}