{"id":"W4386147980","doi":"10.32920/24026664.v1","title":"Sizing biological cells using a microfluidic acoustic flow cytometer","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Research Chairs","keywords":"Ultrasound; Microfluidics; Wavelength; Materials science; Continuous wave; Particle size; Ultrasonic sensor; Coulter counter; Cytometry; Acoustic streaming; Light scattering; Scattering; Optics; Biomedical engineering; Acoustics; Optoelectronics; Nanotechnology; Chemistry; Physics; Cell; Laser; Medicine; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002510034,0.0005938205,0.0006479138,0.0004363536,0.00009393596,0.0001394237,0.0005717949,0.001289624,0.00008956005],"category_scores_gemma":[0.00007239877,0.0004930584,0.000268165,0.0003295382,0.0001683333,0.0000361959,0.001353882,0.00103678,0.0003707237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002266598,"about_ca_system_score_gemma":0.00004687696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005441484,"about_ca_topic_score_gemma":0.000002540404,"domain_scores_codex":[0.9979022,0.00003513436,0.0005138252,0.000666079,0.000179226,0.0007036089],"domain_scores_gemma":[0.9989283,0.0001290186,0.00006117449,0.0007452995,0.0000534449,0.00008275452],"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":[0.000003503393,0.00001192212,0.00004529494,0.0002295787,0.00009629875,0.00008240863,0.00003848626,0.006037852,0.964536,0.000003992762,0.02689201,0.002022631],"study_design_scores_gemma":[0.0001924229,0.00003453449,0.00007109825,0.0004435196,0.0001125722,0.00004375208,0.0001919266,0.1395653,0.8534566,0.0009473797,0.003778364,0.001162628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6615825,0.01035211,0.3106782,0.0001027117,0.004042095,0.0005355801,0.0001499683,0.01220958,0.0003472935],"genre_scores_gemma":[0.9365634,0.01207628,0.05028316,0.00009071793,0.0003396504,0.00001800291,0.00007847491,0.00018356,0.0003668024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2749808,"threshold_uncertainty_score":0.9997521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07988329694528883,"score_gpt":0.2566015886435953,"score_spread":0.1767182916983064,"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."}}