{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002667332,0.0002031643,0.000216918,0.0000712588,0.0004984749,0.0003897495,0.00005628489,0.00009643761,0.00004439669],"category_scores_gemma":[0.0000433493,0.0001898696,0.00001911485,0.00008485888,0.0001083478,0.0004451724,0.00004148075,0.0001095765,0.00001950313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004067651,"about_ca_system_score_gemma":0.00001474029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002954809,"about_ca_topic_score_gemma":0.00001236693,"domain_scores_codex":[0.9989724,0.00003452393,0.0003269609,0.0002791067,0.0001122643,0.000274732],"domain_scores_gemma":[0.9996375,0.00001205418,0.00003979457,0.0001629723,0.00007030452,0.00007732287],"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":[0.00005859664,0.00002186712,0.004094451,0.000203943,0.0000950383,0.000007486275,0.0103494,0.003032595,0.9409285,0.02904422,0.003735454,0.008428493],"study_design_scores_gemma":[0.003915715,0.001796146,0.03918716,0.002487571,0.000186122,0.0003297443,0.005120733,0.2364226,0.6376471,0.007961015,0.06184956,0.003096529],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931403,0.0002236467,0.001717588,0.0001312224,0.003537238,0.0001622597,0.00002497253,0.0002001766,0.0008626257],"genre_scores_gemma":[0.9905152,0.0007113087,0.006810942,0.00004959947,0.001331733,0.00004207854,0.00003384241,0.00004267375,0.0004625724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3032814,"threshold_uncertainty_score":0.7742659,"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."}}