{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004494651,0.00173388,0.001252407,0.001514452,0.000552362,0.002214718,0.001710828,0.001966129,0.08497333],"category_scores_gemma":[0.01048373,0.0004201855,0.0007246044,0.00194361,0.0007639097,0.001393066,0.002014807,0.002297252,0.08928971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001133161,"about_ca_system_score_gemma":0.002705496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006582415,"about_ca_topic_score_gemma":0.005029774,"domain_scores_codex":[0.9970266,0.0008067978,0.0001606245,0.0004289311,0.001428809,0.0001483625],"domain_scores_gemma":[0.9952055,0.001364483,0.00023731,0.0009813419,0.002049678,0.0001616719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002413311,0.0003101605,0.0009628417,0.0003232694,0.0001520501,0.0001153188,0.00004258166,0.04167105,0.004666708,0.02000765,0.262834,0.6686731],"study_design_scores_gemma":[0.0002108419,0.0004009061,0.00317173,0.0002913138,0.00003871012,0.0002840314,0.00005783919,0.5847057,0.01395411,0.02328124,0.373533,0.00007062197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01294892,0.003720232,0.864782,0.003479742,0.002212977,0.0008468096,0.009634987,0.01962726,0.08274703],"genre_scores_gemma":[0.06992593,0.003392948,0.6310068,0.001278693,0.00106021,0.001800918,0.04442306,0.004099065,0.2430124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08497333,"threshold_uncertainty_score":0.2842641,"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."}}