{"id":"W3208830757","doi":"10.1038/s41598-021-00279-6","title":"Numerical investigation of nanofluid flow using CFD and fuzzy-based particle swarm optimization","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Nanofluid Flow and Heat Transfer","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computational fluid dynamics; Nanofluid; Particle swarm optimization; Computer science; Heat flux; Mechanics; Turbulence; Heat transfer; Algorithm; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":{"nature":"Expression of concern","reason":"Concerns/Issues about Authorship/Affiliation;Concerns/Issues about Data;","date":"7/14/2022 0:00","openalex_flagged":false},"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007385306,0.0006008315,0.0007523678,0.0008899698,0.0008375248,0.0007436313,0.0005062334,0.001389103,0.00114116],"category_scores_gemma":[0.001260712,0.0003245178,0.0007687547,0.0005905415,0.0008495823,0.0005375678,0.0004873698,0.000464493,0.00008688041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008593562,"about_ca_system_score_gemma":0.0008573401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01815182,"about_ca_topic_score_gemma":0.005672178,"domain_scores_codex":[0.9997976,0.00006295392,0.00001553031,0.00002616004,0.00006399533,0.00003376175],"domain_scores_gemma":[0.9993621,0.0003749366,0.00006439584,0.00002893039,0.0001241943,0.00004551745],"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":[0.0000442238,0.00003083258,0.001383266,0.00004000236,0.00001297434,0.00007797917,0.00004081278,0.9924864,0.001713114,0.001237419,0.0001164751,0.002816583],"study_design_scores_gemma":[0.000002473473,0.000006154592,0.0001139192,0.000001406968,9.214741e-7,0.000002026432,0.000005418206,0.9996111,0.0001622998,0.0000614758,0.00003093952,0.000001777844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7045777,0.0007298591,0.2775414,0.0008259392,0.0001965697,0.0001944844,0.0002219771,0.0004440252,0.01526813],"genre_scores_gemma":[0.9671458,0.0001207214,0.031339,0.00003098514,0.00001361274,0.00006071291,0.00006499601,0.00001452493,0.001209699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01815182,"threshold_uncertainty_score":0.03609234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01639793224804225,"score_gpt":0.2140381034649162,"score_spread":0.1976401712168739,"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."}}