{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007383209,0.001104677,0.001147931,0.000928829,0.0005047719,0.001549102,0.002124229,0.001796514,0.008673329],"category_scores_gemma":[0.002254723,0.0004153985,0.000769043,0.0005759674,0.0004357088,0.0009964149,0.0006815045,0.001249161,0.001385448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001002792,"about_ca_system_score_gemma":0.001100387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009700558,"about_ca_topic_score_gemma":0.005220495,"domain_scores_codex":[0.9995787,0.0001126398,0.00002126789,0.00009320793,0.0001185448,0.00007573258],"domain_scores_gemma":[0.9991713,0.0004647904,0.0001099742,0.00002970331,0.0001915545,0.00003282471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000787543,0.00003975317,0.0006183542,0.0000633219,0.00002082654,0.00009581158,0.00003687333,0.9809605,0.0007520871,0.007642109,0.0008353894,0.008856227],"study_design_scores_gemma":[0.00001801795,0.00002511534,0.0001516705,0.000009207804,0.00001172126,0.00001850996,0.000007162885,0.9970004,0.000164735,0.001895534,0.0006900034,0.000007915928],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04455152,0.0006223029,0.929038,0.0006233093,0.0001097464,0.0002812234,0.0008627149,0.0009840282,0.02292721],"genre_scores_gemma":[0.9014721,0.0008047991,0.07515711,0.0001287977,0.00008067428,0.001134264,0.0008184089,0.0001187125,0.02028504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009700558,"threshold_uncertainty_score":0.02901518,"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."}}