{"id":"W2523180702","doi":"10.1007/978-3-319-46349-0_33","title":"IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Classifier (UML); Random forest; Baseline (sea); Component (thermodynamics); Data mining","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":[0.0007937801,0.0004217785,0.0004065619,0.0006017157,0.0006309504,0.0003613046,0.002009487,0.0004402242,0.00001618127],"category_scores_gemma":[0.0001183996,0.0003460056,0.0001596873,0.0003351171,0.0003420066,0.0005154579,0.0009439156,0.0007829635,0.00001012662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002737598,"about_ca_system_score_gemma":0.0004222657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000410231,"about_ca_topic_score_gemma":0.00003474155,"domain_scores_codex":[0.997087,0.00002934211,0.0005058836,0.001288769,0.0005145269,0.0005745015],"domain_scores_gemma":[0.9978977,0.0004820781,0.0004385921,0.0008163276,0.0002259648,0.0001393047],"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":[0.000007805484,0.00001777194,0.00006616743,0.0000215379,0.00001371914,0.000005909464,0.0001731818,0.004680146,0.0006439129,0.0311451,0.00001736705,0.9632074],"study_design_scores_gemma":[0.0004626393,0.0003371242,0.000004977632,0.000568821,0.00001322017,0.00004553784,1.837576e-7,0.8546178,0.003855519,0.1138231,0.02558077,0.0006903243],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003645516,0.0003028094,0.9954504,0.001225523,0.0007667008,0.0006959823,0.00001098039,0.0004052072,0.001105951],"genre_scores_gemma":[0.1824194,0.0001401805,0.8121284,0.000424046,0.003007993,0.0001059043,0.000007174093,0.00009921015,0.001667726],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9625171,"threshold_uncertainty_score":0.9998992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03910668136540076,"score_gpt":0.2736425585978755,"score_spread":0.2345358772324747,"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."}}