{"id":"W2475316316","doi":"10.1002/jssc.201600350","title":"Model‐based analysis of a dielectrophoretic microfluidic device for field‐flow fractionation","year":2016,"lang":"en","type":"article","venue":"Journal of Separation Science","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Al Jalila Foundation; Utah Agricultural Experiment Station","keywords":"Microchannel; Levitation; Mechanics; Dielectrophoresis; Drag; Microfluidics; Volumetric flow rate; Microparticle; Voltage; Steady state (chemistry); RADIUS; Materials science; Chemistry; Physics; Optics; Nanotechnology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000195794,0.0004994764,0.0005957496,0.0002956914,0.0005463405,0.0007235808,0.000729069,0.001113054,0.001499605],"category_scores_gemma":[0.0003484395,0.0003760049,0.0006646753,0.0001765265,0.0004372233,0.0004476197,0.0003379406,0.000410064,0.0003097635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168234,"about_ca_system_score_gemma":0.001510305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009425683,"about_ca_topic_score_gemma":0.003854589,"domain_scores_codex":[0.999899,0.00001448844,0.000004176266,0.00002379561,0.00004347811,0.00001502357],"domain_scores_gemma":[0.9998903,0.00005174959,0.0000152204,0.00001016031,0.00002466753,0.000007853899],"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":[0.00002062558,0.00002905308,0.0003908535,0.00005731487,0.00001528622,0.00009119616,0.00003502268,0.9707672,0.02075786,0.003631538,0.0001792394,0.00402485],"study_design_scores_gemma":[0.000002943135,0.00001158053,0.0001240151,0.000002185265,0.00000298673,0.000008635527,0.000002669048,0.9980462,0.001291991,0.0002043969,0.000298396,0.000003954194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1738897,0.0009116108,0.8070309,0.0004645851,0.00007987368,0.000225793,0.0003269365,0.0006249399,0.01644578],"genre_scores_gemma":[0.9459646,0.0005546004,0.04545332,0.00006666401,0.00001586706,0.0004157329,0.0001906296,0.00005589425,0.007282591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009425683,"threshold_uncertainty_score":0.01874161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399597514371893,"score_gpt":0.30253343806118,"score_spread":0.2785374629174611,"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."}}