{"id":"W4307100441","doi":"10.32920/21290979","title":"Sizing biological cells using a microfluidic acoustic flow cytometer","year":2022,"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; Acoustic streaming; Particle size; Continuous wave; Coulter counter; Light scattering; Ultrasonic sensor; Scattering; Optics; Biomedical engineering; Nanotechnology; Chemistry; Optoelectronics; Acoustics; Physics; Laser; Biology; Medicine","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002574476,0.000575454,0.0006253949,0.0003799429,0.0001601124,0.0001142601,0.0006795595,0.0008117815,0.0009618954],"category_scores_gemma":[0.00004283966,0.0005019112,0.0002792087,0.0002895644,0.0001473767,0.00003635789,0.002125654,0.001425564,0.00006195015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003938839,"about_ca_system_score_gemma":0.00005876208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004253287,"about_ca_topic_score_gemma":9.067462e-7,"domain_scores_codex":[0.9979374,0.00005703856,0.0004932285,0.0006552844,0.0002153806,0.0006417027],"domain_scores_gemma":[0.9989474,0.00009695001,0.00006929741,0.0007747478,0.00003634322,0.00007530674],"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":[0.000005579352,0.00002242957,0.00005092773,0.0001724199,0.00009048067,0.00007838102,0.00005316636,0.01073995,0.9698488,0.000006217489,0.01636368,0.002567945],"study_design_scores_gemma":[0.0003080108,0.00007860693,0.00004906057,0.0002009082,0.0001646054,0.0001031363,0.0005219855,0.1237306,0.8408444,0.0006522647,0.03163134,0.001715066],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8068739,0.02314224,0.1589401,0.00006322643,0.003372244,0.0005587304,0.0001862206,0.005606822,0.001256495],"genre_scores_gemma":[0.9431273,0.00867582,0.04739009,0.0001431966,0.000209025,0.00002703213,0.00009282235,0.0001212335,0.000213524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1362533,"threshold_uncertainty_score":0.9999514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04479043959714182,"score_gpt":0.2443050750517862,"score_spread":0.1995146354546444,"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."}}