{"id":"W2915087562","doi":"10.32464/2618-8716-2018-1-2-73-82","title":"Automation of energy management is the key to reduce product cost","year":2019,"lang":"en","type":"article","venue":"Power and Autonomous equipment","topic":"Energy Efficiency and Management","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sydney Steel (Canada)","funders":"","keywords":"Energy management; Energy consumption; Energy accounting; Automation; Efficient energy use; Energy management system; Energy conservation; Energy engineering; Risk analysis (engineering); Energy (signal processing); Computer science; Environmental economics; Operations management; Engineering; Business; Economics; Mechanical engineering; Electrical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002914135,0.000173267,0.0001792319,0.0001092798,0.00009362271,0.00004120679,0.0002520458,0.00002855336,0.0005813202],"category_scores_gemma":[0.000004808662,0.0001262584,0.00005854212,0.0001943199,0.00003763889,0.00007270258,0.0002622929,0.00004562637,0.0001244846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007917477,"about_ca_system_score_gemma":0.00001541689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002356046,"about_ca_topic_score_gemma":0.00002120711,"domain_scores_codex":[0.9986904,0.00004076218,0.0003105266,0.0003903323,0.00027179,0.0002962415],"domain_scores_gemma":[0.9992585,0.00002192122,0.00009395713,0.0005215042,0.00003684557,0.0000672501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007178184,0.0003443543,0.0001505451,0.0001369958,0.0002456875,0.000005887809,0.004317496,0.01696499,0.004147578,0.7866341,0.01337683,0.1736037],"study_design_scores_gemma":[0.000483898,0.0001959952,0.004827782,0.00006268338,0.00004303977,0.000002764531,0.0003697132,0.001144599,0.02875789,0.0008625799,0.9629862,0.0002628063],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3008166,0.0005930391,0.003513811,0.007089464,0.001274981,0.001274593,0.000008808616,0.0001650479,0.6852636],"genre_scores_gemma":[0.9682378,0.00007460617,0.0005779238,0.001634326,0.00002921108,0.0001234769,0.000008108655,0.00001644272,0.02929814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9496094,"threshold_uncertainty_score":0.6365048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009076638996407834,"score_gpt":0.2335747753673261,"score_spread":0.2244981363709182,"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."}}