{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003763154,0.0000907964,0.00009053318,0.00001405902,0.00002362655,0.00002491627,0.0001146578,0.0001063908,0.00002396105],"category_scores_gemma":[0.0001769197,0.0000678625,0.00006273641,0.00006109681,0.00005426365,0.00006727432,0.00001530023,0.00005619837,0.00002885934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005556112,"about_ca_system_score_gemma":0.000009541329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.456045e-7,"about_ca_topic_score_gemma":1.395257e-8,"domain_scores_codex":[0.9994967,7.772914e-7,0.0001250522,0.0001159624,0.00004836002,0.0002131417],"domain_scores_gemma":[0.9996711,0.0001025507,0.000009893374,0.0001532417,0.00003107869,0.00003215865],"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":[0.000003689411,0.000005804915,0.00007282526,0.00002827562,0.00002682108,5.964933e-7,0.000003551041,3.971344e-7,0.9212477,0.0001161921,0.01785101,0.06064313],"study_design_scores_gemma":[0.0001951969,0.00001071386,0.00001680804,0.00002194873,0.00001143416,0.000004677255,0.00001501077,0.00008226137,0.9316772,0.0008641782,0.06698836,0.0001121997],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8935518,0.00245522,0.08216909,0.001132425,0.00009934978,0.0001165368,0.00008253309,0.001737796,0.01865527],"genre_scores_gemma":[0.9953367,0.0001718737,0.0002184346,0.00001230919,0.00006171122,0.00001402396,0.00001379194,0.00002037445,0.004150794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1017849,"threshold_uncertainty_score":0.2767352,"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."}}