{"id":"W2529606079","doi":"10.1021/acs.analchem.6b02915","title":"Digital Microfluidics for Immunoprecipitation","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; SCIEX","keywords":"Microscale chemistry; Microfluidics; Chemistry; Sample preparation; Immunoprecipitation; Analyte; Mass spectrometry; Automation; Nanotechnology; Chromatography; Elution; Lysis; Multiplexing; Sample (material); Computational biology; Computer science; Biochemistry","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":[0.0009563449,0.001147861,0.00070668,0.001221199,0.0006009922,0.001096645,0.001370157,0.0008092903,0.01586566],"category_scores_gemma":[0.001198269,0.0005739477,0.0006753436,0.0006074792,0.0004490523,0.0008868889,0.001267563,0.001371344,0.008446881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066175,"about_ca_system_score_gemma":0.0008759809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005104661,"about_ca_topic_score_gemma":0.0008556296,"domain_scores_codex":[0.9989532,0.0000899904,0.00009807738,0.0003299443,0.000411996,0.0001166932],"domain_scores_gemma":[0.999456,0.0001539694,0.00008297586,0.0001363235,0.0001192138,0.00005156094],"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":[0.000248365,0.0003208309,0.000954024,0.001801523,0.0001188234,0.0002824909,0.0001889652,0.001221168,0.7062503,0.02611239,0.06908186,0.1934192],"study_design_scores_gemma":[0.0001101099,0.0003468159,0.001156192,0.0001498377,0.00007256003,0.0004825183,0.00003155063,0.01144319,0.5531687,0.003900855,0.4290413,0.00009633291],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04464099,0.03363747,0.7591202,0.005040844,0.01116984,0.002982303,0.01546814,0.03124832,0.09669188],"genre_scores_gemma":[0.2112527,0.01644176,0.6944581,0.004539028,0.001848513,0.006079301,0.01049887,0.001365448,0.05351627],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01586566,"threshold_uncertainty_score":0.05307591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005785300062354672,"score_gpt":0.2020530243096546,"score_spread":0.1962677242472999,"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."}}