{"id":"W2952923936","doi":"10.48550/arxiv.1806.05174","title":"Maintenance of Smart Buildings using Fault Trees","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Dependability; Reliability engineering; Fault tree analysis; Computer science; Reliability (semiconductor); Fault tolerance; Markov chain; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0007780603,0.0004153411,0.0004117845,0.0008955047,0.0003459685,0.0009292512,0.0005889199,0.0006868446,0.001460117],"category_scores_gemma":[0.003879339,0.0002374066,0.0006129334,0.0007955589,0.0006376848,0.001419782,0.0005199366,0.0005944391,0.0001315396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001209709,"about_ca_system_score_gemma":0.0006513622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006191405,"about_ca_topic_score_gemma":0.003943573,"domain_scores_codex":[0.9995147,0.0001584216,0.00002478104,0.00008417735,0.0001577603,0.000060134],"domain_scores_gemma":[0.9983028,0.001109865,0.0002795567,0.0001195918,0.0001325761,0.0000555849],"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":[0.00002484174,0.00001426914,0.001353864,0.00002909267,0.00001754779,0.00006200963,0.00004275182,0.9456333,0.0007314974,0.03671695,0.0003665104,0.01500723],"study_design_scores_gemma":[0.000004581967,0.00001055917,0.000280073,0.000007279332,0.000006999845,0.0000239014,0.000007879727,0.9607517,0.0002560919,0.03817296,0.0004744985,0.000003618018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1007469,0.0005452146,0.8944787,0.0003578334,0.00002305278,0.00003869251,0.0002872964,0.0003614394,0.003161082],"genre_scores_gemma":[0.9383313,0.0004417043,0.0599161,0.00003751394,0.00002435033,0.00003951258,0.0002201215,0.00004125046,0.0009481306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006191405,"threshold_uncertainty_score":0.01231074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1920994921323461,"score_gpt":0.2742139080595149,"score_spread":0.08211441592716884,"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."}}