{"id":"W2160851474","doi":"10.1109/tim.2007.909411","title":"Modular Neural Network Architecture for Precise Condition Monitoring","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Modular design; Condition monitoring; Artificial neural network; Process (computing); Reliability engineering; Machining; Fault detection and isolation; Fault (geology); Engineering; Computer science; Machine tool; Embedded system; Real-time computing; Artificial intelligence","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":[0.0001081069,0.000137966,0.0001222125,0.00007982145,0.0002836214,0.00002557163,0.00003363027,0.00005455391,0.00001289338],"category_scores_gemma":[0.000001479746,0.0001416697,0.00006752526,0.00008873373,0.00001999306,0.0001039133,1.92678e-7,0.0001102683,0.000004681175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001211592,"about_ca_system_score_gemma":0.000009842246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009246797,"about_ca_topic_score_gemma":0.00002220286,"domain_scores_codex":[0.9991859,0.00002653749,0.0002064108,0.0001487778,0.000258645,0.0001737257],"domain_scores_gemma":[0.9997343,0.00001510347,0.0000274588,0.00009069941,0.00005179454,0.00008067562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009486735,0.00003304429,0.0000437089,0.00005649505,0.00009376242,8.687567e-7,0.0004118499,0.8541893,0.04392941,0.000008620103,0.0001377433,0.1010003],"study_design_scores_gemma":[0.01066088,0.0009207886,0.005806725,0.0003289586,0.0002272044,0.0001758804,0.0008819799,0.542111,0.4224361,0.0002597813,0.01508434,0.001106362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3956814,0.0001149188,0.6008902,0.00006011185,0.002254962,0.0006099398,0.00001522738,0.000238501,0.000134709],"genre_scores_gemma":[0.9987131,0.00007924687,0.0005450681,0.00003668285,0.0001683143,0.000381413,0.000002998049,0.00002199377,0.00005114338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6030317,"threshold_uncertainty_score":0.5777124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02981804194542728,"score_gpt":0.2336575784698234,"score_spread":0.2038395365243961,"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."}}