{"id":"W4292014099","doi":"10.1021/acs.langmuir.2c01255","title":"Machine Learning-Aided Microdroplets Breakup Characteristic Prediction in Flow-Focusing Microdevices by Incorporating Variations of Cross-Flow Tilt Angles","year":2022,"lang":"en","type":"article","venue":"Langmuir","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Breakup; Microfluidics; Body orifice; Flow focusing; Volumetric flow rate; Computer science; Flow (mathematics); Lift (data mining); Materials science; Simulation; Mechanics; Mechanical engineering; Nanotechnology; Machine learning; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005321494,0.0004929836,0.0003189408,0.000308113,0.0001771008,0.0003705104,0.0003291954,0.0005110355,0.0003133442],"category_scores_gemma":[0.001004145,0.0002169179,0.0003764492,0.0002042144,0.0001615652,0.0003385941,0.0002365191,0.0004623662,0.00008348816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004322154,"about_ca_system_score_gemma":0.0004531885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004912517,"about_ca_topic_score_gemma":0.003921734,"domain_scores_codex":[0.9998884,0.00002252895,0.000009544198,0.00003487695,0.00002878538,0.00001588257],"domain_scores_gemma":[0.9996519,0.0001925208,0.00005042832,0.00001990083,0.00007323714,0.00001198164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001031575,0.0001174165,0.0034484,0.00004291031,0.00002533938,0.00004812335,0.00004064449,0.9140643,0.02125674,0.0002539292,0.0001208687,0.06047809],"study_design_scores_gemma":[0.00000114568,0.00001895045,0.0003576117,0.000001162426,0.000002447337,0.000002321334,0.000002020323,0.996415,0.003134198,0.00003677598,0.00002624286,0.000001998885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5902084,0.0003362102,0.4073029,0.0001105798,0.00002421037,0.00005745848,0.00007188148,0.0006186701,0.001269713],"genre_scores_gemma":[0.9679294,0.00008999234,0.03131627,0.00001880626,0.000003549933,0.00004556512,0.00006147876,0.00000916462,0.0005257461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004912517,"threshold_uncertainty_score":0.00976789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008163880661896194,"score_gpt":0.2253953197695474,"score_spread":0.2172314391076512,"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."}}