{"id":"W2603644504","doi":"10.1016/j.ijheatmasstransfer.2017.03.058","title":"A predictive-corrective process for predicting forced convective heat transfer in heated tubes at supercritical pressures","year":2017,"lang":"en","type":"article","venue":"International Journal of Heat and Mass Transfer","topic":"Heat transfer and supercritical fluids","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Nuclear Laboratories","funders":"National Science Fund for Distinguished Young Scholars","keywords":"Supercritical fluid; Heat transfer; Materials science; Convective heat transfer; Mechanics; Thermodynamics; Convection; Buoyancy; Dimensionless quantity; Flow (mathematics); Heat transfer coefficient; Churchill–Bernstein equation; Nusselt number; Reynolds number; Physics; Turbulence","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":[0.0003160577,0.000272391,0.0004895433,0.0002085045,0.0001738376,0.0001336423,0.0003643674,0.0001895459,0.00005413204],"category_scores_gemma":[0.0001344766,0.0002399863,0.0002060102,0.00004109355,0.0001985214,0.0008136328,0.00001092766,0.000410637,8.272439e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001479739,"about_ca_system_score_gemma":0.0000583455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005343504,"about_ca_topic_score_gemma":0.0001319154,"domain_scores_codex":[0.9982541,0.00004949202,0.0006131415,0.0002461092,0.0004400078,0.0003971775],"domain_scores_gemma":[0.9988117,0.000327561,4.85896e-7,0.0001166719,0.0005153416,0.0002282002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006249695,0.0003994938,0.1198898,0.0007137881,0.001622711,0.0003559827,0.01193972,0.00339564,0.8513462,0.003101567,0.0001347952,0.0008505709],"study_design_scores_gemma":[0.01192151,0.001331493,0.04244734,0.001136947,0.0003273171,0.0004967612,0.00120824,0.06236739,0.8759903,0.001772244,0.0003020929,0.0006983619],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8946625,0.0004868305,0.1014846,0.0007768086,0.0009618004,0.0004086075,0.0001947365,0.00004450417,0.000979631],"genre_scores_gemma":[0.9991542,0.0002862715,0.00003747055,0.00008033571,0.0003142088,0.00006390239,0.0000106773,0.00004563622,0.000007299735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1044917,"threshold_uncertainty_score":0.9786358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486046680484374,"score_gpt":0.2793011673849702,"score_spread":0.2644407005801265,"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."}}