{"id":"W3205760234","doi":"10.1115/omae2021-63031","title":"Flow of Yield Power Law Fluids in Horizontal Pipes-Flow Field Characterization Using Particle Image Velocimetry Technique","year":2021,"lang":"en","type":"article","venue":"Volume 10: Petroleum Technology","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; University of Alberta","funders":"","keywords":"Laminar flow; Particle image velocimetry; Materials science; Mechanics; Rheology; Plug flow; Yield (engineering); Flow (mathematics); Pipe flow; Drilling fluid; Reynolds number; Spark plug; Power law; Thermodynamics; Composite material; Drilling; Physics; Turbulence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001383984,0.0002150261,0.000359336,0.000301644,0.00003960638,0.000023907,0.000192227,0.0004123741,0.0002410676],"category_scores_gemma":[0.0001039108,0.000264697,0.00007775669,0.000761556,0.0000634024,0.0001734588,0.00008516363,0.0004081826,0.00002641757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001024919,"about_ca_system_score_gemma":0.00002763511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000323743,"about_ca_topic_score_gemma":0.00002214576,"domain_scores_codex":[0.9987549,0.00001431034,0.0004065431,0.0002742549,0.0001293164,0.000420656],"domain_scores_gemma":[0.9994031,0.00003451591,0.00004148621,0.0004098229,0.00006294025,0.00004819711],"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":[0.00000819738,0.00004511893,0.001508282,0.00007179964,0.00002705797,0.00006432679,0.0000419844,0.01915175,0.9760891,0.0002197778,0.00004963564,0.002722938],"study_design_scores_gemma":[0.0001670557,0.00006660863,0.000186262,0.0000878143,0.000009833339,0.00003637557,0.0000405475,0.4222251,0.5759099,0.0000503147,0.001040523,0.0001795788],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6085184,0.0002610104,0.3891657,0.0001345439,0.0003136735,0.00008770933,0.00002116736,0.0006793796,0.0008184841],"genre_scores_gemma":[0.9816079,0.00003274714,0.01805935,0.00002174111,0.00004295378,0.00003136925,0.0000224103,0.00005881578,0.0001227302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4030734,"threshold_uncertainty_score":0.9999805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004978867519134212,"score_gpt":0.1916399771347538,"score_spread":0.1866611096156195,"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."}}