{"id":"W3036385411","doi":"10.1101/2020.06.22.166074","title":"MRBLES 2.0: High-throughput generation of chemically functionalized spectrally and magnetically-encoded hydrogel beads using a simple single-layer microfluidic device","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Electrical, Communications and Cyber Systems; Natural Sciences and Engineering Research Council of Canada; Stanford Bio-X; Novo Nordisk Fonden; Cancer Research Institute; Novo Nordisk; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Institutes of Health; National Science Foundation","keywords":"Bioconjugation; Microfluidics; Analyte; Bead; Materials science; Nanotechnology; Surface modification; Polymer; Polymerization; Polystyrene; Dispersity; Throughput; Chemistry; Chromatography; Computer science; Polymer chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005006152,0.0005724857,0.0003767517,0.0003683629,0.0001428199,0.0005105131,0.0003947912,0.0003794638,0.001359106],"category_scores_gemma":[0.0003196193,0.0004391519,0.0002512519,0.0001173013,0.0002628491,0.0003748582,0.0004737751,0.0005452543,0.0008133251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003911861,"about_ca_system_score_gemma":0.0004035877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002841531,"about_ca_topic_score_gemma":0.0006125348,"domain_scores_codex":[0.9997471,0.00002786829,0.00001796267,0.00007887107,0.00009677578,0.00003134696],"domain_scores_gemma":[0.9998648,0.00002674066,0.00004259523,0.00001679168,0.00001771148,0.00003133858],"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":[0.00002584063,0.00001104326,0.00003297678,0.00001894548,0.000002316744,0.00001247302,0.000007962583,0.0001378208,0.9974418,0.0001936028,0.00009810958,0.002017276],"study_design_scores_gemma":[0.000008707833,0.00002935795,0.00009355903,0.000001436289,0.000001735694,0.00002470031,0.000001289337,0.001766764,0.9965418,0.0000259212,0.001497636,0.000007049679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5803494,0.001877586,0.4037926,0.0005316131,0.0002829493,0.0007743172,0.001613802,0.005833774,0.004943985],"genre_scores_gemma":[0.5878351,0.001076613,0.39443,0.0002631653,0.00007768121,0.000874856,0.001678398,0.0004822083,0.01328205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001359106,"threshold_uncertainty_score":0.004546642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03484417676879559,"score_gpt":0.231079901473296,"score_spread":0.1962357247045004,"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."}}