{"id":"W4291366336","doi":"10.3390/app12168081","title":"On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":466,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Trois-Rivières; Université du Québec à Rimouski","funders":"","keywords":"Predictive maintenance; Prognostics; Downtime; Workflow; Industry 4.0; Condition-based maintenance; Context (archaeology); Predictive analytics; Engineering; Risk analysis (engineering); Production (economics); Computer science; Reliability engineering; Data science; Data mining; Business","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.002666213,0.00009185371,0.0001732373,0.000105319,0.001533839,0.000004407761,0.0002536338,0.000137751,0.0001577836],"category_scores_gemma":[0.00005398018,0.00007875118,0.00001261278,0.0003537698,0.0002118925,0.00008539221,0.0002545857,0.001349217,0.00001225623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350524,"about_ca_system_score_gemma":0.000275402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002386602,"about_ca_topic_score_gemma":0.0002842241,"domain_scores_codex":[0.9980242,0.0004659095,0.0002872649,0.0003873393,0.0004067371,0.0004285018],"domain_scores_gemma":[0.9988939,0.0007397158,0.0001070241,0.0001589665,0.0000186234,0.00008174446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007490986,0.00004483973,0.001903388,0.0001315061,0.000002131665,0.000002594108,0.01643074,0.00306071,0.000003945068,0.9736797,0.0006628446,0.004002639],"study_design_scores_gemma":[0.0009989035,0.00048871,0.04648221,0.0002331302,0.00000390142,0.000002658444,0.2554223,0.01016445,0.000004062363,0.6724342,0.0134672,0.0002982934],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7616544,0.005465834,0.00004855711,0.04533826,0.0006205227,0.00140956,0.00009264856,0.0001041012,0.1852661],"genre_scores_gemma":[0.9935385,0.001488547,0.00006761151,0.00419908,0.00004744595,0.0004940971,0.000002133046,0.0000055378,0.0001570585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3012456,"threshold_uncertainty_score":0.9997661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3400403376815759,"score_gpt":0.4494295485135332,"score_spread":0.1093892108319573,"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."}}