{"id":"W3200158131","doi":"10.3390/mi12091102","title":"Numerical and Experimental Validation of Mixing Efficiency in Periodic Disturbance Mixers","year":2021,"lang":"en","type":"article","venue":"Micromachines","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; École de Technologie Supérieure; McGill University","funders":"Khalifa University of Science, Technology and Research; Natural Sciences and Engineering Research Council of Canada; École de technologie supérieure","keywords":"Micromixer; Mixing (physics); Computer science; Process (computing); Reliability (semiconductor); Image processing; Software; Image (mathematics); Artificial intelligence; Physics","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":[0.00003188086,0.00007949685,0.0001179841,0.0000347902,0.00003284397,0.00001271105,0.00004841989,0.00002809716,0.00003490663],"category_scores_gemma":[0.00000584626,0.00008172152,0.00002365223,0.0001814246,0.00003142337,0.00003321241,0.00001434954,0.00005415759,0.000002438434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002528243,"about_ca_system_score_gemma":0.00001061599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002302317,"about_ca_topic_score_gemma":0.000001018571,"domain_scores_codex":[0.9995537,0.0000155978,0.0001487254,0.0001258503,0.00004988083,0.0001062801],"domain_scores_gemma":[0.9998385,0.00001467319,0.00001485449,0.00009791741,0.00001114001,0.000022932],"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":[0.000002294088,0.00003528627,0.004089644,0.00002624888,0.000004811689,0.000002460248,0.0004467517,0.000112478,0.9945207,0.00006237483,0.0004934555,0.0002034559],"study_design_scores_gemma":[0.0001655633,0.00001060205,0.007239103,0.00001436049,0.000003585441,0.00001789255,0.0001040413,0.0006918652,0.9905165,0.00002166245,0.001127488,0.00008738101],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9189196,0.07990896,0.0007798403,0.0000271602,0.00003285428,0.00004992231,0.000004136585,0.00002692675,0.000250608],"genre_scores_gemma":[0.9979391,0.001830722,0.0001446398,0.00001095047,0.00001289692,0.00001170109,0.00001858287,0.00001087852,0.00002059269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07901944,"threshold_uncertainty_score":0.3332507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004455013877789372,"score_gpt":0.2123501498004157,"score_spread":0.2078951359226263,"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."}}