{"id":"W3129858191","doi":"10.1002/cjce.24074","title":"Prediction and analysis of thermal‐hydraulic performance of tubes with teardrop dimples based on artificial neural networks","year":2021,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Heat Transfer Mechanisms","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Dimple; Pressure drop; Nusselt number; Materials science; Heat transfer; Mechanics; Artificial neural network; Heat transfer enhancement; Flow (mathematics); Structural engineering; Engineering; Composite material; Computer science; Physics; Heat transfer coefficient; Artificial intelligence; Turbulence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001403455,0.0001020979,0.0002558099,0.0001854476,0.0000206254,0.00001399432,0.00009337619,0.00005598617,0.00001688326],"category_scores_gemma":[0.00002277092,0.00007758474,0.00007458438,0.0004287525,0.00003763081,0.00005230445,0.00000278842,0.0002579697,5.181179e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000529723,"about_ca_system_score_gemma":0.00007741623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006275172,"about_ca_topic_score_gemma":0.0001153729,"domain_scores_codex":[0.9993341,0.00001175176,0.0002726335,0.00005865042,0.0001473832,0.0001755019],"domain_scores_gemma":[0.9995582,0.00007031513,0.00001916201,0.0001169025,0.00006964022,0.0001658491],"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":[0.00001272737,0.000002886777,0.0007210713,0.00003592225,0.0001707056,0.000009336162,0.0000860669,0.8540825,0.1442221,0.00002864162,0.000001639527,0.0006263389],"study_design_scores_gemma":[0.00009509115,0.00004523034,0.002529607,0.00006590875,0.0001859653,0.00001522523,0.000006274867,0.7358308,0.2611694,0.000001189548,0.000004061419,0.00005128937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932166,0.0001637005,0.006403807,0.00006214432,0.0000702053,0.00003058772,0.00001358377,0.00001024562,0.00002918047],"genre_scores_gemma":[0.9997119,0.00000446778,0.0001877317,0.00001453584,0.00005743087,0.000001035324,0.00000406854,0.00001846833,3.856709e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1182517,"threshold_uncertainty_score":0.3163814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008086382914557237,"score_gpt":0.1627906518736309,"score_spread":0.1547042689590737,"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."}}