{"id":"W2043514335","doi":"10.1109/icif.2006.301598","title":"Fault Diagnosis Using Multi-Source Information Fusion","year":2006,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Fault (geology); Computer science; Reliability (semiconductor); Information fusion; Data mining; Representation (politics); Fuzzy logic; Artificial intelligence; Knowledge representation and reasoning; Sensor fusion; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001798219,0.00126932,0.001816781,0.004975549,0.0007033581,0.001784184,0.001072417,0.001601886,0.001153747],"category_scores_gemma":[0.006386695,0.0004267616,0.001288488,0.002778469,0.0005525787,0.003033359,0.00185846,0.0009875859,0.0003187526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009171182,"about_ca_system_score_gemma":0.0005983541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001565856,"about_ca_topic_score_gemma":0.001099574,"domain_scores_codex":[0.998321,0.000456416,0.0001459819,0.0002727867,0.0007162851,0.00008746835],"domain_scores_gemma":[0.9977803,0.001173747,0.000235207,0.0002176685,0.0005454741,0.00004770829],"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":[0.0008178394,0.0001626622,0.002716823,0.0008274755,0.0005123036,0.001005429,0.0003796182,0.3489145,0.0251197,0.01737839,0.002423402,0.5997419],"study_design_scores_gemma":[0.00004093907,0.00008529532,0.000830523,0.0000612919,0.0001239996,0.0002054448,0.00005509275,0.973055,0.008984391,0.01526108,0.001245655,0.00005126534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01307479,0.001059102,0.9834221,0.0002452991,0.00007603921,0.00005980989,0.00009663526,0.0005093673,0.001456856],"genre_scores_gemma":[0.7130544,0.001203395,0.2841625,0.0001541036,0.0001246944,0.0001178061,0.0003344105,0.00003970713,0.0008090967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004975549,"threshold_uncertainty_score":0.009509981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008752687622167517,"score_gpt":0.206232633952671,"score_spread":0.1974799463305034,"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."}}