{"id":"W2042034585","doi":"10.1021/ac900522a","title":"A Digital Microfluidic Approach to Proteomic Sample Processing","year":2009,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Chemistry; Microfluidics; Digital microfluidics; Nanotechnology; Sample (material); Chromatography; Computational biology; Electrowetting","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":[0.0005371389,0.0006425532,0.0004976376,0.0008933456,0.0005874696,0.001052318,0.001077366,0.0006681039,0.002518565],"category_scores_gemma":[0.0006590676,0.0004108342,0.0004630417,0.0006176193,0.0005741092,0.000751219,0.001118979,0.0008628873,0.001081376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008166431,"about_ca_system_score_gemma":0.000787305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003697967,"about_ca_topic_score_gemma":0.0005618449,"domain_scores_codex":[0.9994113,0.00005451008,0.00005238305,0.0001785506,0.0002586334,0.00004455412],"domain_scores_gemma":[0.9997935,0.00005657372,0.00003080419,0.00004493568,0.00005141795,0.0000229177],"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":[0.0001018528,0.0001132476,0.0003196712,0.0007366646,0.0000507032,0.0001769004,0.00007245479,0.001294623,0.8710162,0.01927366,0.005444268,0.1013998],"study_design_scores_gemma":[0.00005639853,0.0003497047,0.0007746805,0.00008191833,0.0000602092,0.001088334,0.00002407565,0.01136061,0.7761763,0.005350798,0.2045828,0.00009417679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03433122,0.02035462,0.9173711,0.002178354,0.00344856,0.0009159731,0.002050386,0.003451954,0.01589786],"genre_scores_gemma":[0.1370349,0.0179031,0.8244827,0.001843917,0.0009547916,0.001285431,0.001166682,0.0001264393,0.01520198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002518565,"threshold_uncertainty_score":0.008425415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007338381126256431,"score_gpt":0.209633960547264,"score_spread":0.2022955794210076,"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."}}