{"id":"W4385887913","doi":"10.55274/r0011029","title":"PR-261-15609-R02 Machine Learning Algorithms for Smart Meter Diagnostics � Part III (TR2777)","year":2017,"lang":"en","type":"report","venue":"","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Chemicals (Canada)","funders":"","keywords":"Header; Algorithm; Data set; Flow (mathematics); Computer science; Artificial intelligence; Mathematics; Data mining; Statistics; Geometry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001219477,0.0006911381,0.001142998,0.000290702,0.000332365,0.0002603756,0.0004864455,0.0005129646,0.0007845801],"category_scores_gemma":[0.001052087,0.0006107757,0.0007359196,0.0001104395,0.00004519605,0.0001595337,0.00009896306,0.0007639804,0.0001196116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002306306,"about_ca_system_score_gemma":0.0001719198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003385743,"about_ca_topic_score_gemma":0.0003997879,"domain_scores_codex":[0.9969817,0.00003127319,0.0007630575,0.0005575774,0.0009814856,0.0006849113],"domain_scores_gemma":[0.9980476,0.0002419142,0.0002726354,0.000786479,0.000452949,0.00019843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001744662,0.0001097565,0.005025468,0.001711548,0.004816602,0.00009215535,0.0001003489,0.004936965,0.000170904,0.00002499402,0.8526019,0.1303919],"study_design_scores_gemma":[0.0005022796,0.00005922753,0.00009649045,0.0002659323,0.001178492,0.000008980042,0.000009990549,0.06809922,0.0006206917,0.00002357865,0.9283332,0.0008018677],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0007434089,0.04212435,0.1761691,0.0004131767,0.01980496,0.003237339,0.0007289718,0.003545866,0.7532328],"genre_scores_gemma":[0.03956523,0.0886273,0.01827161,0.0001353831,0.0123824,0.001262118,0.007224167,0.001306712,0.8312251],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1578975,"threshold_uncertainty_score":0.9996344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07317901784751052,"score_gpt":0.2826218530497057,"score_spread":0.2094428352021951,"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."}}