{"id":"W1975969686","doi":"10.1016/j.nucengdes.2014.05.001","title":"Assessment of subchannel code ASSERT-PV for flow-distribution predictions","year":2014,"lang":"en","type":"article","venue":"Nuclear Engineering and Design","topic":"Heat transfer and supercritical fluids","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Atomic Energy (Canada)","funders":"","keywords":"Flow (mathematics); Sensitivity (control systems); Distribution (mathematics); Environmental science; Code (set theory); Nuclear engineering; Set (abstract data type); Mechanics; Computer science; Engineering; Mathematics; Physics","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":[0.00125602,0.0006485652,0.0005469022,0.0003643417,0.0004797708,0.0007841176,0.001849979,0.001005779,0.004555454],"category_scores_gemma":[0.006410083,0.0003216932,0.0003763748,0.0003318716,0.0003014648,0.001057343,0.0007449225,0.0009855133,0.0009227871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007067446,"about_ca_system_score_gemma":0.001930708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01730521,"about_ca_topic_score_gemma":0.01312246,"domain_scores_codex":[0.9995396,0.0001380243,0.00002221269,0.00007022371,0.0001837831,0.00004629431],"domain_scores_gemma":[0.995331,0.002373437,0.0001548491,0.00051545,0.001379784,0.0002454638],"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":[0.001369907,0.0006641558,0.01159445,0.0001967127,0.00009779687,0.0001356985,0.000127176,0.9024727,0.01121459,0.004462742,0.004354944,0.06330905],"study_design_scores_gemma":[0.00002349707,0.00005705518,0.0004401388,0.000005227273,0.000004544263,0.000004891107,0.00001191802,0.9963887,0.002537223,0.0002140246,0.0003072305,0.000005723657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8490362,0.0002470323,0.1151413,0.0004604651,0.0001931143,0.0002144742,0.002418918,0.01010406,0.02218449],"genre_scores_gemma":[0.959953,0.00006148557,0.03642124,0.0001089659,0.00002005857,0.00009735739,0.00100765,0.0006908728,0.001639357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01730521,"threshold_uncertainty_score":0.03440893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388767146291102,"score_gpt":0.2223578275705629,"score_spread":0.2084701561076518,"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."}}