{"id":"W2602114365","doi":"10.1038/s41598-017-00229-1","title":"Simulation-assisted design of microfluidic sample traps for optimal trapping and culture of non-adherent single cells, tissues, and spheroids","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; CMC Microsystems","keywords":"Spheroid; Microfluidics; Trapping; Trap (plumbing); Biomedical engineering; Materials science; Parametric statistics; Biological system; Sample (material); Computer science; Nanotechnology; Chemistry; Chromatography; Physics; Mathematics; Biology; In vitro","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":[0.001037297,0.0001101456,0.0002217288,0.00008064424,0.0002269092,0.0001753567,0.0001386315,0.00009269486,0.00002689586],"category_scores_gemma":[0.0005596671,0.0001007347,0.00004248902,0.0001015151,0.0004520857,0.0001093952,0.00006235381,0.0000769746,3.206264e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002085543,"about_ca_system_score_gemma":0.00003980198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000324868,"about_ca_topic_score_gemma":0.000002248358,"domain_scores_codex":[0.9987179,0.00001662121,0.000409335,0.0003235734,0.0003048997,0.0002276546],"domain_scores_gemma":[0.9988745,0.0002068744,0.0001783123,0.0004777331,0.000152578,0.0001099674],"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":[0.000008502055,0.00003711109,0.0004117062,0.000349817,0.00002711601,0.000006979197,0.0006132844,0.004188863,0.9699296,0.000001235731,0.002014285,0.02241149],"study_design_scores_gemma":[0.0003607339,0.0001139223,0.002763925,0.0002218489,0.00002867759,0.00001231085,0.00009182523,0.1063294,0.8763455,0.0004166117,0.01313952,0.0001756849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7428018,0.0015058,0.2536733,0.00001779915,0.001145008,0.0006649028,0.00001671533,0.00004008553,0.0001344998],"genre_scores_gemma":[0.9686197,0.00003496703,0.03102992,6.20719e-7,0.00002506402,0.00001089409,0.00001255466,0.00001656749,0.0002497218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2258178,"threshold_uncertainty_score":0.4107843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04951031591949717,"score_gpt":0.3075559794989353,"score_spread":0.2580456635794381,"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."}}