{"id":"W2093698202","doi":"10.1039/c1cc12371h","title":"Predictive measure of quality of micromixing","year":2011,"lang":"en","type":"article","venue":"Chemical Communications","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Micromixing; Microreactor; Yield (engineering); Measure (data warehouse); Chemistry; Product (mathematics); Diffusion; Quality (philosophy); Biological system; Thermodynamics; Chromatography; Analytical Chemistry (journal); Physics; Organic chemistry; Mathematics; Computer science; Catalysis; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.005536939,0.000824312,0.0009344246,0.002287238,0.0005323592,0.002459318,0.001363033,0.001099772,0.001369695],"category_scores_gemma":[0.0321252,0.0004243552,0.0004284602,0.001483651,0.002598738,0.004740225,0.002139672,0.001097931,0.000320218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001242571,"about_ca_system_score_gemma":0.0007947183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009589727,"about_ca_topic_score_gemma":0.0006600033,"domain_scores_codex":[0.9960055,0.0005835885,0.000209686,0.0008417411,0.002041059,0.0003185585],"domain_scores_gemma":[0.967824,0.01767896,0.006967285,0.003712078,0.003005046,0.0008126292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001360507,0.0003609807,0.1133121,0.0005257832,0.0003247465,0.000377596,0.0004476876,0.5438407,0.1067021,0.05817385,0.001653563,0.1729203],"study_design_scores_gemma":[0.00002833691,0.000465407,0.02069383,0.00004625841,0.00006142672,0.000250302,0.00009759118,0.8785072,0.06905559,0.02882773,0.001828625,0.0001376886],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3606554,0.001203176,0.6304525,0.0003946717,0.0000861681,0.00008351356,0.0006107918,0.001580835,0.004932964],"genre_scores_gemma":[0.9771786,0.0001659786,0.02170394,0.00005744152,0.000042099,0.00004665422,0.0003316077,0.00007410707,0.0003995674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005536939,"threshold_uncertainty_score":0.02928251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08465713680959296,"score_gpt":0.292329914652724,"score_spread":0.207672777843131,"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."}}