{"id":"W4224218712","doi":"10.3389/fbioe.2022.878398","title":"Neural Network-Based Optimization of an Acousto Microfluidic System for Submicron Bioparticle Separation","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioengineering and Biotechnology","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":14,"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":"Microfluidics; Bandwidth (computing); Transducer; Computer science; Artificial neural network; Electronic engineering; Resonator; Acoustics; Materials science; Nanotechnology; Engineering; Electrical engineering; Artificial intelligence; Optoelectronics","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":[0.0005021344,0.0007908405,0.0005840587,0.0003519952,0.0002986738,0.0006645052,0.0005851673,0.000875147,0.001653153],"category_scores_gemma":[0.0007078204,0.0004249282,0.0004722814,0.000273586,0.0004303089,0.000414602,0.0004383422,0.0005882013,0.0001620534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008700855,"about_ca_system_score_gemma":0.001061019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008351773,"about_ca_topic_score_gemma":0.007191503,"domain_scores_codex":[0.9998123,0.00003907792,0.00001008007,0.00005034116,0.00005535933,0.00003284427],"domain_scores_gemma":[0.9997413,0.0001442436,0.00003722366,0.000006566395,0.00006027735,0.00001043689],"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":[0.00003327654,0.00002204292,0.0002043882,0.00002793851,0.00001246648,0.00001957097,0.00000873909,0.9895453,0.001577839,0.0003832675,0.00009617012,0.008069019],"study_design_scores_gemma":[0.000002205572,0.0000150816,0.00004261433,0.000001154937,0.000002785821,0.000001262676,0.00000140397,0.9995807,0.000253011,0.00005369721,0.0000449644,0.000001095371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1686315,0.0009440467,0.8166839,0.0003752756,0.00008834314,0.0001558128,0.0001062756,0.000636799,0.012378],"genre_scores_gemma":[0.9412151,0.000206324,0.05470869,0.00008238889,0.00001422432,0.0002319876,0.00006911445,0.00002339565,0.003448741],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008351773,"threshold_uncertainty_score":0.01660633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006650036427315257,"score_gpt":0.195644109555883,"score_spread":0.1889940731285677,"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."}}