{"id":"W2110721942","doi":"10.3138/infor.45.2.83","title":"Model for the Selection of Predictive Maintenance Techniques","year":2007,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Predictive maintenance; Computer science; Set (abstract data type); Model predictive control; Selection (genetic algorithm); Process (computing); Quality (philosophy); Risk analysis (engineering); Reliability engineering; Control (management); Engineering; Machine learning; Artificial intelligence; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002259803,0.00006300522,0.00009102218,0.0001936465,0.0002197397,0.000119895,0.00007695178,0.00007377406,0.000002535923],"category_scores_gemma":[0.0001229063,0.00004352154,0.00002633341,0.0001968376,0.00004730558,0.0006988921,0.00001155914,0.0001277459,0.000004351617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008058048,"about_ca_system_score_gemma":0.00005270943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008668115,"about_ca_topic_score_gemma":0.00003710084,"domain_scores_codex":[0.9989661,0.00001337601,0.000420636,0.00004412162,0.0003860447,0.0001697213],"domain_scores_gemma":[0.9987686,0.0002527477,0.00004137243,0.00007316133,0.0008253696,0.00003870211],"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":[0.0004737349,0.00001612033,0.0005845247,0.0008609754,0.0001701643,1.238885e-7,0.004384534,0.7380025,0.005597221,0.1630753,0.02339044,0.06344435],"study_design_scores_gemma":[0.0002003579,0.00005326739,0.0003581348,0.00002928241,0.000001794337,0.000007227536,0.0009777007,0.9326395,0.001149538,0.00002835211,0.0645096,0.00004524218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007411541,0.0001466624,0.9777007,0.00007372772,0.0002136852,0.001626404,0.00006719428,0.0001068063,0.01265334],"genre_scores_gemma":[0.9986529,0.00004111214,0.0003010585,0.00002565069,0.00009625651,0.0003714888,0.00001177948,0.000005424225,0.0004942781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9912414,"threshold_uncertainty_score":0.1774757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03232644398529738,"score_gpt":0.3175668667859075,"score_spread":0.2852404228006101,"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."}}