{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002999187,0.0003124168,0.0002669384,0.0003060434,0.0002215049,0.0003528118,0.0002891427,0.00036497,0.0006538087],"category_scores_gemma":[0.0002889919,0.0001387836,0.0002162569,0.0001214917,0.0003580562,0.0004691198,0.0002329243,0.000274952,0.0002436992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002838679,"about_ca_system_score_gemma":0.0003288939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004048009,"about_ca_topic_score_gemma":0.0007667965,"domain_scores_codex":[0.9998937,0.00001393033,0.000009896057,0.00002545934,0.00003786726,0.0000191478],"domain_scores_gemma":[0.9998461,0.00007340865,0.00003713095,0.00001123576,0.00002174005,0.00001030708],"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":[0.00001598825,0.00001862337,0.0001895221,0.00009792935,0.00000614386,0.00002481871,0.00001461182,0.0009375235,0.9913014,0.0005304131,0.0001001257,0.006763014],"study_design_scores_gemma":[0.000007766987,0.00007420516,0.0005430296,0.000005501324,0.00000703773,0.00004902421,0.000007853941,0.008514066,0.9886914,0.00012575,0.001965588,0.000008666582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7884199,0.006145591,0.200326,0.0005205174,0.0002496362,0.0001902693,0.0003341735,0.0003968197,0.003417095],"genre_scores_gemma":[0.896994,0.00349985,0.09528183,0.0002321448,0.00008114034,0.0001550387,0.0004222225,0.00003320806,0.003300474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006538087,"threshold_uncertainty_score":0.002187192,"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."}}