{"id":"W4387591817","doi":"10.1016/j.nucengdes.2023.112643","title":"ASYST4.1 validation for gas cooled SMR applications using the HTTF experiment","year":2023,"lang":"en","type":"article","venue":"Nuclear Engineering and Design","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering","keywords":"Nuclear engineering; Coolant; Natural circulation; Modular design; Thermal hydraulics; Transient (computer programming); Molten salt; Nuclear reactor; Heat transfer; Scram; Thermal conduction; Helium; Environmental science; Materials science; Mechanical engineering; Engineering; Thermodynamics; Computer science; Chemistry; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001524417,0.0001510665,0.0001263436,0.00008033845,0.0001165163,0.00008839661,0.00009942654,0.00005414796,0.000005705253],"category_scores_gemma":[0.000009632084,0.0001376543,0.00004283649,0.0002502149,0.00001103707,0.00008206486,0.00002248093,0.00009558893,0.00002539044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003482002,"about_ca_system_score_gemma":0.000004785991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002876142,"about_ca_topic_score_gemma":4.190663e-8,"domain_scores_codex":[0.9993781,0.00000637249,0.0001366486,0.0001402616,0.00009228461,0.0002463256],"domain_scores_gemma":[0.9996248,0.00008672616,0.00001453205,0.0001879403,0.00002086215,0.00006507657],"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":[0.000003422984,0.000005118523,8.668212e-7,0.00008588071,0.00004029114,6.474211e-7,0.0002822379,0.7804301,0.2124495,0.004371344,0.0007148851,0.001615709],"study_design_scores_gemma":[0.000154936,0.00001651559,0.00002258654,0.00002401235,0.00001757968,0.000004609501,0.00009403002,0.9531683,0.005325742,0.00009816972,0.04089925,0.0001742854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1111397,0.0003300033,0.8843732,0.00005745328,0.0004417196,0.001000495,0.00001595635,0.00225292,0.0003885338],"genre_scores_gemma":[0.991184,0.00008018337,0.008192215,0.000009068585,0.0002267564,0.0001331704,0.00001122008,0.0001420425,0.00002137639],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8800443,"threshold_uncertainty_score":0.561338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03089155954388607,"score_gpt":0.2289869040835137,"score_spread":0.1980953445396276,"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."}}