{"id":"W2142993974","doi":"10.1002/pmic.201100608","title":"Digital microfluidic hydrogel microreactors for proteomics","year":2012,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Occupational Cancer Research Centre; University of Toronto","funders":"","keywords":"Trypsin; Chromatography; Agarose; Chemistry; Sample preparation; Reagent; Microreactor; Microfluidics; Self-healing hydrogels; Proteomics; Proteolytic enzymes; Digestion (alchemy); Immobilized enzyme; Materials science; Enzyme; Biochemistry; Nanotechnology; Organic chemistry","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":[0.0006642172,0.0005983188,0.0003387115,0.0003942413,0.0002192607,0.0004762051,0.0007981156,0.0004448413,0.003154218],"category_scores_gemma":[0.0007023283,0.0002972404,0.0002956432,0.0002947561,0.0003248017,0.0006478121,0.000483987,0.0005171098,0.0008844462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009528375,"about_ca_system_score_gemma":0.000408708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003458541,"about_ca_topic_score_gemma":0.0008474289,"domain_scores_codex":[0.9995951,0.00005099718,0.00002700589,0.0001102841,0.0001846331,0.00003202151],"domain_scores_gemma":[0.9997074,0.0001286276,0.00007000873,0.00003559531,0.00003552667,0.00002292908],"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":[0.00005974917,0.00002862603,0.0002549338,0.0003055258,0.00001651294,0.00004965142,0.00002456647,0.000361358,0.9608847,0.002407869,0.001449943,0.03415653],"study_design_scores_gemma":[0.00004271615,0.0002138357,0.001439929,0.00003839118,0.00003589792,0.0003242945,0.00001365047,0.007500845,0.9524845,0.0007306469,0.03713689,0.00003854599],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1855693,0.05031671,0.7356212,0.001972615,0.002177395,0.0007988842,0.002706687,0.005617731,0.01521945],"genre_scores_gemma":[0.3739859,0.01348095,0.5979848,0.00102571,0.0003655529,0.0007975981,0.0009711037,0.0001523121,0.01123601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003154218,"threshold_uncertainty_score":0.01055193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009191487846630882,"score_gpt":0.2036138915825932,"score_spread":0.1944224037359623,"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."}}