{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001806869,0.000274602,0.0002316364,0.0001206135,0.00009456697,0.00007323926,0.000282773,0.000237051,0.000003534978],"category_scores_gemma":[0.0001041917,0.0002759449,0.00012355,0.0001545463,0.0000714941,0.0003218695,0.00005781901,0.00026665,0.00005968021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001545026,"about_ca_system_score_gemma":0.00002691323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002338745,"about_ca_topic_score_gemma":1.959516e-7,"domain_scores_codex":[0.9986448,0.000004236282,0.0002769709,0.0001924016,0.0000799167,0.0008016739],"domain_scores_gemma":[0.9994583,0.00004358629,0.00004882431,0.0003095455,0.0000369622,0.0001028545],"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":[0.00001028389,0.00003815387,0.001672118,0.000090942,0.00004728007,3.699032e-7,0.0001365705,0.000002529827,0.9761281,0.0003461095,0.008242762,0.01328477],"study_design_scores_gemma":[0.0003090915,0.00004377448,0.00003779419,0.00002111784,0.00001515153,0.00002663344,0.00004924765,0.00006329957,0.9235379,0.0006069646,0.07496291,0.0003261878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9305485,0.008225611,0.05678387,0.00009070896,0.0003798776,0.001355803,0.00009148362,0.001754523,0.0007695982],"genre_scores_gemma":[0.9739376,0.0005544724,0.02419886,0.00004459526,0.0002930732,0.0004960675,0.00005879076,0.0001327401,0.0002838208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06672015,"threshold_uncertainty_score":0.9999692,"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."}}