{"id":"W2997278206","doi":"10.1088/1361-6501/ab6761","title":"Echo-Lagrangian particle tracking: an ultrasound-based method for extracting path-dependent flow quantities","year":2020,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pulsatile flow; Reynolds number; Velocimetry; Particle image velocimetry; Mechanics; Flow (mathematics); Particle tracking velocimetry; Acoustics; Amplitude; Particle displacement; Tracking (education); Ultrasound; Path length; Physics; Optics; Turbulence","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":[0.0006022642,0.0003322937,0.0002533916,0.001013938,0.0002220172,0.0006561702,0.0004603403,0.0004255887,0.00117744],"category_scores_gemma":[0.0009936246,0.0002488365,0.0001764429,0.0005906203,0.0003561516,0.0005013265,0.0004338486,0.0004316517,0.0005308211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003152532,"about_ca_system_score_gemma":0.0007661829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009008092,"about_ca_topic_score_gemma":0.001131887,"domain_scores_codex":[0.9998401,0.00002838185,0.00001153245,0.00003351914,0.0000749846,0.00001137498],"domain_scores_gemma":[0.9996409,0.0001277395,0.00009829215,0.00003638312,0.00007929679,0.00001733216],"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":[0.0001805896,0.0001011761,0.003274729,0.0002499655,0.00003828473,0.0002454098,0.0001846618,0.03236569,0.6868401,0.008264054,0.001450236,0.2668051],"study_design_scores_gemma":[0.00003115645,0.0001119486,0.003130899,0.00002739181,0.00002551289,0.000298399,0.00002958929,0.7300994,0.2590278,0.001641331,0.00552498,0.00005152011],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01938676,0.0001047562,0.9792392,0.00003490455,0.00001575246,0.00003793767,0.0000585983,0.0005690925,0.0005531025],"genre_scores_gemma":[0.1438825,0.000254409,0.8535513,0.0000567449,0.00001811002,0.0001157913,0.0001448494,0.0001285619,0.00184766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00117744,"threshold_uncertainty_score":0.003938913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06429141701328618,"score_gpt":0.2708775783936299,"score_spread":0.2065861613803437,"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."}}