{"id":"W4401809293","doi":"10.3390/mi15091063","title":"Offsetting Dense Particle Sedimentation in Microfluidic Systems","year":2024,"lang":"en","type":"article","venue":"Micromachines","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NIHR Imperial Biomedical Research Centre; Canadian Association of Medical Oncologists; Imperial College London; National Institute for Health and Care Research; Department of Health and Social Care; Government of the United Kingdom; University of Warwick; Wellcome Trust","keywords":"Settling; Sedimentation; Metering mode; Mechanics; Particle size; Materials science; Microfluidics; Particle (ecology); Fluidics; Drop (telecommunication); Nanotechnology; Environmental science; Mechanical engineering; Engineering; Physics; Chemical engineering; Environmental engineering; Geology; Aerospace engineering; Sediment","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008086286,0.0004726706,0.0004458803,0.000424554,0.000281423,0.0005537138,0.0005024876,0.0004811278,0.00038753],"category_scores_gemma":[0.001459035,0.0003330847,0.0002882654,0.0002174959,0.0006135149,0.0006379714,0.0008159551,0.0005375848,0.0002434048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007579958,"about_ca_system_score_gemma":0.0006741455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009471185,"about_ca_topic_score_gemma":0.001128437,"domain_scores_codex":[0.9994986,0.00009585297,0.00004189623,0.00009255758,0.0002137388,0.00005730617],"domain_scores_gemma":[0.9994023,0.0002553847,0.0001669977,0.000068096,0.00007643661,0.00003069104],"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":[0.00007696493,0.00005102026,0.0007741291,0.000385593,0.00002762195,0.0001184054,0.0001269512,0.01861658,0.9331109,0.006635758,0.0002979483,0.03977825],"study_design_scores_gemma":[0.00005752822,0.0004634133,0.001486447,0.00003407144,0.00004173039,0.0001500955,0.00002596492,0.1461149,0.8406138,0.002754035,0.008207768,0.00005032465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5305998,0.007032282,0.4555627,0.0004885299,0.0002357671,0.0002173025,0.0000879254,0.002079683,0.003696023],"genre_scores_gemma":[0.8902214,0.002222255,0.105756,0.0001379485,0.00006660007,0.0001271228,0.00008469733,0.00005940363,0.001324567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009471185,"threshold_uncertainty_score":0.005499661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009125591528849408,"score_gpt":0.2233137282944337,"score_spread":0.2141881367655843,"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."}}