{"id":"W4236020682","doi":"10.1007/978-3-642-21822-4_18","title":"Discovering Patterns for Prognostics: A Case Study in Prognostics of Train Wheels","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Prognostics; Computer science; Data mining; Component (thermodynamics); Machine learning; Reliability engineering; Artificial intelligence; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001269226,0.0004943698,0.0007957445,0.0008483622,0.0001531172,0.0003057729,0.001970115,0.0001839122,0.000008086105],"category_scores_gemma":[0.0002358059,0.0004367229,0.000186098,0.0005979456,0.0003110802,0.0005118541,0.001131222,0.0004713001,0.000001420564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001252347,"about_ca_system_score_gemma":0.0003142487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003737648,"about_ca_topic_score_gemma":0.002652827,"domain_scores_codex":[0.9964163,0.00003906139,0.0009421654,0.001327637,0.0006139645,0.0006608391],"domain_scores_gemma":[0.9974064,0.0006873155,0.0005136972,0.001005669,0.0002661159,0.000120788],"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":[0.00001813215,0.000393815,0.01054971,0.000270416,0.00006250264,0.002337039,0.0194574,0.02328733,0.00003044061,0.005278421,0.000001640986,0.9383132],"study_design_scores_gemma":[0.001107901,0.00228627,0.001216558,0.001451974,0.0000866367,0.0007551502,0.00003518986,0.95575,0.0005488895,0.03532945,0.00009254609,0.001339353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01662039,0.0001282461,0.9810757,0.00004310198,0.0004516824,0.001414804,0.00002310929,0.00004674369,0.0001962076],"genre_scores_gemma":[0.8377168,0.000007133416,0.161979,0.00004936811,0.0001369895,0.0000379048,0.000002903163,0.00002999199,0.00003988063],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9369738,"threshold_uncertainty_score":0.9998084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03743374576646853,"score_gpt":0.2587193889350811,"score_spread":0.2212856431686125,"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."}}