{"id":"W2023945384","doi":"10.2202/1556-3758.1079","title":"Friction Factor Prediction for Newtonian and Non-Newtonian Fluids in Pipe Flows Using Neural Networks","year":2007,"lang":"en","type":"article","venue":"International Journal of Food Engineering","topic":"Rheology and Fluid Dynamics Studies","field":"Chemical Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Bingham plastic; Non-Newtonian fluid; Power law; Thermodynamics; Viscosity; Power-law fluid; Absolute deviation; Standard deviation; Apparent viscosity; Mathematics; Newtonian fluid; Approximation error; Mechanics; Yield (engineering); Rheology; Physics; Materials science; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008239662,0.0006789057,0.0005121976,0.001011015,0.0002343018,0.0005196836,0.0003848268,0.0004613184,0.0006665048],"category_scores_gemma":[0.002781437,0.0003118933,0.0005645329,0.0005941647,0.0002438266,0.0007606565,0.0002470359,0.000454497,0.0002214373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007952966,"about_ca_system_score_gemma":0.0005204066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01115941,"about_ca_topic_score_gemma":0.007282007,"domain_scores_codex":[0.9998035,0.0000325832,0.00001701588,0.00006233173,0.00005790895,0.00002663818],"domain_scores_gemma":[0.9992813,0.0004274549,0.00008026617,0.00003105694,0.0001593403,0.00002049562],"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":[0.0003706983,0.000104281,0.01440479,0.000121453,0.00003595572,0.00009695962,0.00007460935,0.8508242,0.02049459,0.0007139488,0.0004631869,0.1122955],"study_design_scores_gemma":[0.000002173582,0.00002105419,0.001714006,0.00000327827,0.00000346788,0.000004618148,0.000003490355,0.9943218,0.003669502,0.0001663122,0.00008507552,0.00000513166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6331031,0.0004921546,0.3628107,0.0001019675,0.00006456047,0.00006643429,0.0002645343,0.001152394,0.001944062],"genre_scores_gemma":[0.9655435,0.0001626688,0.03292506,0.00001054349,0.000007515747,0.00004216847,0.0001982077,0.00003288873,0.001077501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01115941,"threshold_uncertainty_score":0.0221889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009603262498080302,"score_gpt":0.2347673123149379,"score_spread":0.2251640498168576,"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."}}