{"id":"W2931177318","doi":"10.1038/s41598-019-41938-z","title":"Efficient Low Shear Flow-based Trapping of Biological Entities","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Concordia University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Trapping; Flow (mathematics); Computer science; Shear (geology); Mechanics; Materials science; Biology; Physics; Ecology; Composite material","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":[],"consensus_categories":[],"category_scores_codex":[0.0005418279,0.0001239679,0.0002076104,0.0001881399,0.00006402517,0.00005604777,0.0001190737,0.0001163894,0.0001568545],"category_scores_gemma":[0.00005337014,0.00009828813,0.0001021985,0.000340067,0.0002980251,0.00002124095,0.00004231312,0.00009515386,0.00004404146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003173517,"about_ca_system_score_gemma":0.00003608011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003509665,"about_ca_topic_score_gemma":4.029199e-7,"domain_scores_codex":[0.998787,0.00001202929,0.0003649608,0.0003414059,0.0002323968,0.0002621363],"domain_scores_gemma":[0.999229,0.00002443905,0.00007008166,0.000581994,0.00006052131,0.00003401944],"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":[0.000003169386,0.00004158573,0.00168671,0.0001243958,0.00001219975,0.00005319557,0.00008957214,0.03254837,0.95289,0.00005360513,0.01112026,0.001376899],"study_design_scores_gemma":[0.0001059949,0.0000321305,0.0005495053,0.0001398587,0.000006769808,0.00003794319,0.0001617438,0.06431571,0.9212881,0.0003937089,0.01275907,0.0002094079],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886594,0.0005445565,0.002229178,0.00002590973,0.006638357,0.0001784082,0.000003281188,0.0005232639,0.001197578],"genre_scores_gemma":[0.9981785,0.00000646173,0.001357706,0.000005636184,0.00001407981,0.000002476686,0.00001500471,0.00001023959,0.0004099093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03176735,"threshold_uncertainty_score":0.4008074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224618621867351,"score_gpt":0.1944109319016154,"score_spread":0.1821647456829419,"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."}}