{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001655529,0.000147007,0.0001378764,0.0001385397,0.0000388059,0.0001149035,0.00009437578,0.00009068014,0.0000103723],"category_scores_gemma":[0.00001378409,0.0001300899,0.00003764746,0.0003235416,0.00003608418,0.0001086028,0.00003128867,0.0001530575,0.0001221397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008225043,"about_ca_system_score_gemma":0.00001015255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001246094,"about_ca_topic_score_gemma":0.000008443874,"domain_scores_codex":[0.9992342,0.00002177188,0.0002472826,0.0001839417,0.00006998293,0.0002428466],"domain_scores_gemma":[0.9997686,0.00004454905,0.00001061974,0.0001415336,0.00001074775,0.00002397623],"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":[0.000002282535,0.000008675534,0.00211656,0.0001673792,0.00001565432,0.00005839242,0.0003011747,0.00006406823,0.9647244,0.0001890131,0.02627889,0.006073487],"study_design_scores_gemma":[0.0002790855,0.00002467018,0.001880753,0.0004107926,0.00002417362,0.0001604365,0.0005322121,0.0467467,0.9157929,0.0001960011,0.03359555,0.0003566948],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8878343,0.1088068,0.0008664143,0.0001955446,0.0007043011,0.0001181802,0.00001038158,0.00134443,0.0001196899],"genre_scores_gemma":[0.9977466,0.001773043,0.000200579,0.0000232858,0.00007589594,0.00000969848,0.00001501734,0.00003190243,0.0001239677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1099123,"threshold_uncertainty_score":0.5304911,"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."}}