{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004789449,0.0001733485,0.0001767833,0.00003603406,0.00004716562,0.0001042153,0.0002348699,0.0001378227,0.000008712702],"category_scores_gemma":[0.0001380933,0.0001686427,0.00006044623,0.0002989502,0.00003852212,0.00007631331,0.00002669314,0.0002526897,0.00002559445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008090092,"about_ca_system_score_gemma":0.00002432499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000100608,"about_ca_topic_score_gemma":8.059314e-9,"domain_scores_codex":[0.9990683,0.000001525233,0.0001787709,0.0002465713,0.000106603,0.0003982393],"domain_scores_gemma":[0.9996139,0.00001452991,0.00001213205,0.0002260856,0.00002450842,0.0001088648],"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":[0.000005555961,0.00005461704,0.00008579265,0.0000955194,0.00001626211,0.000003321917,0.00003195809,0.00002256113,0.9249029,0.00004206667,0.01317984,0.06155964],"study_design_scores_gemma":[0.0001355742,0.00002528305,0.00007910839,0.0000383868,0.00001647469,0.00003127606,0.00004978959,0.001534723,0.9846486,0.000463786,0.01268123,0.0002957296],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8547376,0.002607161,0.05831186,0.000542079,0.00001625357,0.0002204533,0.00001992608,0.002573729,0.08097094],"genre_scores_gemma":[0.9972556,0.00002974342,0.001933103,0.00008963314,0.00007037992,0.00001252579,0.00002064801,0.00002083006,0.0005674854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1425181,"threshold_uncertainty_score":0.6877051,"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."}}