{"id":"W2767223182","doi":"10.1016/j.jobe.2017.10.013","title":"A practical solution for HVAC prognostics: Failure mode and effects analysis in building maintenance","year":2017,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; National Research Council Canada","funders":"","keywords":"HVAC; Prognostics; Reliability engineering; Failure mode and effects analysis; Engineering; Fault detection and isolation; Work (physics); Fault (geology); Computer science; Risk analysis (engineering); Air conditioning; Artificial intelligence; Mechanical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007916892,0.0009385179,0.0007909261,0.0007303377,0.0008233308,0.0009903036,0.001051219,0.00185398,0.009627803],"category_scores_gemma":[0.00381966,0.0004391816,0.0005663748,0.0004187993,0.0005354423,0.001527985,0.001200121,0.001295303,0.001584669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000394721,"about_ca_system_score_gemma":0.001357149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002435733,"about_ca_topic_score_gemma":0.002378395,"domain_scores_codex":[0.9996104,0.0001047565,0.00002049725,0.00008108394,0.0001418672,0.00004145085],"domain_scores_gemma":[0.9992566,0.0002823125,0.00003952344,0.0001064014,0.0002821217,0.00003314764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001512987,0.0001845958,0.002110845,0.0002662447,0.00007233275,0.0002767181,0.0001813159,0.5762567,0.01152737,0.04667242,0.0113415,0.3509588],"study_design_scores_gemma":[0.0000154823,0.00004939404,0.000327465,0.00001669959,0.00001307684,0.00008153423,0.00004392368,0.97175,0.001115876,0.02428905,0.002286315,0.00001119521],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006656243,0.0001430385,0.9900208,0.0004726012,0.00007407575,0.00005213486,0.00007555506,0.000405448,0.002100116],"genre_scores_gemma":[0.4315847,0.0005165716,0.55611,0.0002172759,0.0003133526,0.0002548083,0.0003293876,0.000144909,0.01052892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009627803,"threshold_uncertainty_score":0.03220814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008337461997711626,"score_gpt":0.2599226580734746,"score_spread":0.251585196075763,"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."}}