{"id":"W2899790678","doi":"10.1115/ipc2018-78179","title":"Investigation on Drag Reduction Through Dimple Machining","year":2018,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Fluid Dynamics and Thin Films","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Materials science; Dimple; Drag; Pressure drop; Machining; Reynolds number; Silicone oil; Mechanics; Composite material; Viscosity; Metallurgy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001405194,0.0002512256,0.0002137228,0.0002682382,0.0001772678,0.0003305495,0.0002041083,0.0002616434,0.0007467495],"category_scores_gemma":[0.0004563542,0.000156323,0.0001651205,0.0002272896,0.0002595742,0.0002801666,0.0001487454,0.0002493987,0.0001225344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002030725,"about_ca_system_score_gemma":0.0001255324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002796393,"about_ca_topic_score_gemma":0.0005614597,"domain_scores_codex":[0.9998453,0.00001344121,0.000006033524,0.00002172913,0.00008573867,0.00002779869],"domain_scores_gemma":[0.9997469,0.00009042179,0.00006577642,0.00003395102,0.00004636714,0.00001660613],"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":[0.00006921371,0.00004565768,0.0007350048,0.00008694327,0.000004413209,0.00008408575,0.00005040805,0.001058277,0.9889789,0.0001708305,0.0000577703,0.008658557],"study_design_scores_gemma":[0.0000144871,0.0007497483,0.007441209,0.00000743637,0.00001299004,0.0001590979,0.00007187716,0.01071464,0.9789858,0.00007979137,0.00175138,0.00001169055],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995566,0.0003052658,0.002740859,0.00002498169,0.00001082743,0.00001267984,0.00002414813,0.00003490128,0.001280341],"genre_scores_gemma":[0.991939,0.0003555257,0.006852715,0.00001299727,0.000004305031,0.000006078117,0.00004032374,0.000008807518,0.0007802876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007467495,"threshold_uncertainty_score":0.00249815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02018612875850803,"score_gpt":0.226690353310771,"score_spread":0.206504224552263,"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."}}