{"id":"W2896123694","doi":"10.1016/j.anucene.2018.08.038","title":"Phenomena identification and ranking table study for thermal hydraulics for Advanced High Temperature Reactor","year":2018,"lang":"en","type":"article","venue":"Annals of Nuclear Energy","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Nuclear Safety Commission","funders":"Oak Ridge National Laboratory; Office of Nuclear Energy; Ohio State University; Nuclear Energy University Program; University of Michigan; U.S. Department of Energy","keywords":"Thermal hydraulics; Blackout; Hydraulics; Nuclear engineering; Coolant; Control rod; Research reactor; Environmental science; Ranking (information retrieval); Computer science; Engineering; Mechanical engineering; Heat transfer; Nuclear physics; Physics; Thermodynamics; Aerospace 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":[0.00009586827,0.0001267337,0.0001801406,0.00005097648,0.00006341574,0.00004285919,0.0001027545,0.00005076209,0.000006010558],"category_scores_gemma":[0.0000142729,0.0001290122,0.00003986961,0.00009937072,0.00002054008,0.0001872408,0.00002057653,0.00004389808,0.000001135797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008556842,"about_ca_system_score_gemma":0.000003799641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001815654,"about_ca_topic_score_gemma":0.000006071469,"domain_scores_codex":[0.9993802,0.000005245764,0.000180302,0.0001581269,0.00007829604,0.0001978122],"domain_scores_gemma":[0.9995712,0.00003268906,0.00004545863,0.000185587,0.0001200074,0.00004502778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000807999,0.00008391023,0.000004789753,0.00008786026,0.0001362937,2.374696e-7,0.0005963258,0.002862725,0.9503539,0.01413403,0.00148573,0.03017337],"study_design_scores_gemma":[0.003660761,0.001906489,0.004213098,0.0001649543,0.0001294446,0.000002422452,0.001156623,0.1154508,0.2757017,0.003092833,0.5932587,0.001262265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978717,0.0001200211,0.0005101765,0.00005700837,0.0002845928,0.0002233642,0.00002961156,0.0001263822,0.0007771422],"genre_scores_gemma":[0.9989143,0.00005879045,0.0004416092,0.00005371346,0.0003572884,0.00002470764,0.00001685368,0.00009126677,0.00004145113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6746522,"threshold_uncertainty_score":0.5260963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537691600701411,"score_gpt":0.2361184388149387,"score_spread":0.2207415228079246,"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."}}