{"id":"W4389889720","doi":"10.3390/s23249900","title":"Particle Tracking and Micromixing Performance Characterization with a Mobile Device","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto Tecnológico y de Estudios Superiores de Monterrey; Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología; University of Ottawa","keywords":"Micromixer; Micromixing; Multiphysics; Mixing (physics); Homogeneity (statistics); Computer science; Reynolds number; Tracking (education); Characterization (materials science); Computational fluid dynamics; Materials science; Biological system; Simulation; Mechanics; Mechanical engineering; Microfluidics; Engineering; Nanotechnology; Physics; Finite element method; Structural engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006157931,0.0004826983,0.000367612,0.0006632908,0.0003026602,0.000527757,0.0004067299,0.000517449,0.001693186],"category_scores_gemma":[0.0007415811,0.0001707946,0.0002977773,0.0003425296,0.0002290241,0.0003156857,0.0003443712,0.0004381853,0.0004792164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004342218,"about_ca_system_score_gemma":0.0004150115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009133074,"about_ca_topic_score_gemma":0.001117197,"domain_scores_codex":[0.9996656,0.00002174504,0.00002573315,0.00007478535,0.0001808635,0.00003126747],"domain_scores_gemma":[0.9996259,0.0001365649,0.00006982653,0.00004959231,0.00009531921,0.00002273116],"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":[0.0001361319,0.00007206993,0.001182676,0.0001127182,0.00001316887,0.0000641164,0.0001247404,0.003263488,0.9670254,0.0006336287,0.0002966497,0.02707515],"study_design_scores_gemma":[0.00001144942,0.0003849808,0.001866958,0.000008907323,0.00001772695,0.00007213696,0.0000357188,0.02922158,0.9661053,0.000104595,0.002143526,0.00002720138],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6566304,0.0007720621,0.3344322,0.0002427529,0.0001204402,0.0004160475,0.0006976676,0.002477565,0.004210814],"genre_scores_gemma":[0.7639948,0.0004481497,0.2311233,0.00008675384,0.00002330839,0.0003342782,0.0003438745,0.0001073991,0.003538111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001693186,"threshold_uncertainty_score":0.005664289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01147553999892565,"score_gpt":0.1915812560568361,"score_spread":0.1801057160579105,"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."}}