{"id":"W2805632990","doi":"10.1038/s41598-018-21833-9","title":"An integrated microfluidic device for the sorting of yeast cells using image processing","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Alchemy (Canada)","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Microfluidics; Sorting; Computer science; Cell sorting; Process (computing); Image processing; Yeast; Division (mathematics); Computer hardware; Artificial intelligence; Image (mathematics); Embedded system; Nanotechnology; Cell; Biology; Materials science","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":[0.0007434242,0.0001155733,0.0001297778,0.00006623919,0.0005283131,0.0002038604,0.0001766341,0.00004525228,0.00004457876],"category_scores_gemma":[0.00002261868,0.00008815122,0.00005585987,0.0005253905,0.0002933268,0.0001423041,0.00001758919,0.0000659284,0.000004020797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003980627,"about_ca_system_score_gemma":0.0001227299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003569348,"about_ca_topic_score_gemma":0.000005331706,"domain_scores_codex":[0.9988225,0.00001292666,0.0004336824,0.0003107569,0.0001561997,0.0002639257],"domain_scores_gemma":[0.9988798,0.00001551294,0.0001588321,0.0005359505,0.0003573884,0.00005246167],"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":[0.000002102579,0.00001346075,0.00001899239,0.00004384053,0.000009975771,0.000001536862,0.0003135541,0.00002152832,0.9634149,0.000004766316,0.03303156,0.003123788],"study_design_scores_gemma":[0.00003527026,0.00001238294,0.00001996687,0.0000315867,0.00003457128,0.00003358772,0.0002691598,0.01266479,0.936905,0.0002074846,0.04968693,0.00009932725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8488551,0.01339028,0.1357277,0.000009877542,0.001174654,0.0004782837,0.000008637239,0.0001286679,0.0002267594],"genre_scores_gemma":[0.997172,0.0002336339,0.002252027,0.00001069377,0.0001214821,0.00002595408,0.00002475325,0.00003419825,0.0001252359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1483169,"threshold_uncertainty_score":0.4063411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01397530438973262,"score_gpt":0.2547757977520115,"score_spread":0.2408004933622789,"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."}}