{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005280523,0.00007599843,0.00007422567,0.00004283852,0.00005988157,0.00002789099,0.00003299983,0.00004235771,0.000002772465],"category_scores_gemma":[0.00000522034,0.00006418007,0.000007634984,0.0002390769,0.00003481682,0.00006865108,0.00001607677,0.0000622833,0.00003330285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001138148,"about_ca_system_score_gemma":0.000002703273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001985107,"about_ca_topic_score_gemma":0.000001340612,"domain_scores_codex":[0.9996079,0.000004694001,0.0000762597,0.000097718,0.00004496009,0.0001684501],"domain_scores_gemma":[0.9998537,0.00001288566,0.00001211029,0.00008935035,0.00001327328,0.00001871661],"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":[0.000004574988,0.000002348319,0.005874334,0.00005309652,0.00000789463,0.000007715873,0.0004372167,0.0008160819,0.961695,0.000005529474,0.00003819907,0.03105803],"study_design_scores_gemma":[0.0001643677,0.00004074087,0.0390105,0.00007609699,0.000009578574,0.00003195155,0.0005107172,0.07890028,0.8778334,0.000002933172,0.003250445,0.0001689999],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984616,0.0001058646,0.00009058632,0.00004285191,0.00004210224,0.00008054432,0.000002700604,0.00112855,0.00004521531],"genre_scores_gemma":[0.9991032,0.0005340592,0.0002246951,0.00001223642,0.00001824828,0.000004352142,0.000008381732,0.00001786944,0.00007688408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0838616,"threshold_uncertainty_score":0.2617188,"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."}}