{"id":"W2949915337","doi":"10.3762/bxiv.2019.17.v1","title":"Investigation on drag reduction performance of pipeline with bioinspired microgrooved surface","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Natural Science Foundation of Shandong Province; China Scholarship Council; National Natural Science Foundation of China","keywords":"Drag; Reduction (mathematics); Materials science; Turbulence; Mechanics; Vortex; Drag coefficient; Pipeline (software); Shear stress; Engineering; Mechanical engineering; Composite material; Physics; Geometry; Mathematics","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":[0.0001107853,0.0003184607,0.0001716126,0.0002353413,0.0001465926,0.0002204424,0.000168045,0.0002853051,0.0003431593],"category_scores_gemma":[0.0002025384,0.0001127491,0.0002537383,0.0002075298,0.000187828,0.0003029405,0.0001632941,0.0001830347,0.00008233401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001770312,"about_ca_system_score_gemma":0.0001425642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004276696,"about_ca_topic_score_gemma":0.0007576756,"domain_scores_codex":[0.9999224,0.000005191297,0.000003598699,0.0000189012,0.00002946988,0.00002052391],"domain_scores_gemma":[0.9999148,0.00001263952,0.00002276492,0.00001050356,0.0000275342,0.00001178101],"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":[0.0000307842,0.00002176516,0.0003733835,0.00006002656,0.000003636789,0.0000436566,0.00002107436,0.0009209999,0.9955229,0.0001053908,0.00003164318,0.00286482],"study_design_scores_gemma":[0.000007580647,0.0005821821,0.004765907,0.000005852599,0.0000172753,0.0001197783,0.00005416939,0.009410793,0.983595,0.00004718016,0.001379378,0.00001504554],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993489,0.0004448917,0.005365763,0.0000289952,0.00002064255,0.00001022301,0.00004650166,0.00006684277,0.0005272021],"genre_scores_gemma":[0.9923196,0.0003506238,0.006606501,0.00001477215,0.00000450733,0.00001107383,0.0000612264,0.00000900834,0.0006225464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004276696,"threshold_uncertainty_score":0.00128442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752391641079573,"score_gpt":0.204881684876988,"score_spread":0.1873577684661923,"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."}}