{"id":"W4250450747","doi":"10.32920/14640339","title":"Shrinking microbubbles with microfluidics: mathematical modelling to control microbubble sizes","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Fluid Dynamics and Mixing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Royal Society; California HIV/AIDS Research Program","keywords":"Microbubbles; Microfluidics; Bubble; Nanotechnology; Materials science; Computer science; Mechanics; Ultrasound; Acoustics; 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":[0.0009080604,0.001100427,0.0009481609,0.0007771752,0.0004701451,0.0008872963,0.001380929,0.002501536,0.001651543],"category_scores_gemma":[0.002000674,0.0006332382,0.001623982,0.0005497466,0.0009956957,0.00147958,0.000793849,0.001558216,0.0004787826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001430967,"about_ca_system_score_gemma":0.001160063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00774258,"about_ca_topic_score_gemma":0.004533727,"domain_scores_codex":[0.9997506,0.00006907246,0.00001538702,0.00004197775,0.00009120957,0.00003188046],"domain_scores_gemma":[0.9992648,0.0004939283,0.000109407,0.00002678401,0.00008244283,0.00002272811],"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":[0.00001059942,0.00002995399,0.0002161873,0.00006295117,0.00001209119,0.00002987636,0.00004809664,0.9778947,0.004073149,0.01534299,0.000210712,0.002068553],"study_design_scores_gemma":[0.00000229555,0.000004070674,0.00002104765,0.000002726865,0.000001489154,0.000003673029,0.000001520616,0.9985964,0.0002357728,0.000820904,0.0003067155,0.000003420751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0279827,0.001595189,0.9591577,0.0009971877,0.0001425256,0.0001773765,0.0002131473,0.0003367613,0.009397468],"genre_scores_gemma":[0.7039184,0.005205174,0.2610742,0.0007672531,0.000294388,0.001796153,0.0003920927,0.0004055787,0.02614672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00774258,"threshold_uncertainty_score":0.01539505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008397163647837477,"score_gpt":0.1851916498330874,"score_spread":0.17679448618525,"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."}}