{"id":"W4389890633","doi":"10.32920/24625185","title":"Microfluidic Platform for Intracellular Liquid-liquid Phase Separation Studies","year":2023,"lang":"en","type":"preprint","venue":"","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Public Health Ontario","funders":"","keywords":"Microfluidics; Liquid liquid; Reagent; Phase (matter); Organelle; In vitro; Chromatography; Chemistry; Nanotechnology; Computer science; Computational biology; Biology; Biological system; Materials science; Biochemistry","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":[0.0002621946,0.0005705068,0.0003874047,0.0002986914,0.000308721,0.000523228,0.0005366768,0.0004596705,0.003036689],"category_scores_gemma":[0.0002670158,0.0002240076,0.0002244211,0.0002053526,0.000207102,0.0003294736,0.0004714221,0.0006919308,0.001620721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005048729,"about_ca_system_score_gemma":0.0005425585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000283251,"about_ca_topic_score_gemma":0.0003971215,"domain_scores_codex":[0.9997531,0.00002249752,0.00001458,0.00009524582,0.0000789907,0.00003558265],"domain_scores_gemma":[0.9998817,0.00004273307,0.00002290873,0.00001641519,0.00002024926,0.0000159858],"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":[0.00005728719,0.00006601153,0.0001154334,0.0001005143,0.000006896063,0.00005745363,0.00002533684,0.0006205371,0.984271,0.003004753,0.001143033,0.01053185],"study_design_scores_gemma":[0.00002984738,0.000158753,0.0004153035,0.00001701194,0.00001366782,0.00008020143,0.00001411989,0.009734657,0.9682273,0.0007102774,0.02058047,0.00001838346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3177257,0.005872724,0.6385095,0.001375645,0.00131786,0.00121605,0.005035899,0.005213634,0.02373303],"genre_scores_gemma":[0.5444046,0.004073807,0.4307446,0.000511539,0.0002058387,0.002899937,0.003003493,0.0002360392,0.01392012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003036689,"threshold_uncertainty_score":0.01015872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08185938785817119,"score_gpt":0.4058180858755768,"score_spread":0.3239586980174056,"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."}}