{"id":"W2057061545","doi":"10.1021/ac5045127","title":"Tunable Electrophoretic Separations Using a Scalable, Fabric-Based Platform","year":2015,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Grand Challenges Canada","keywords":"Electrophoresis; Analyte; Microfluidics; Buffer (optical fiber); Chemistry; Scalability; Resolution (logic); Chromatography; Nanotechnology; Materials science; Computer science; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002785534,0.0006067971,0.0003215662,0.0003807572,0.0002149078,0.000532861,0.0005705854,0.0004850345,0.0006106409],"category_scores_gemma":[0.0002738942,0.0003017005,0.0003152881,0.0001996592,0.0002783755,0.0005871103,0.0005677546,0.0006624051,0.0005971993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003356783,"about_ca_system_score_gemma":0.000206869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001871512,"about_ca_topic_score_gemma":0.0003293834,"domain_scores_codex":[0.9997693,0.00002029005,0.00001555237,0.00006808645,0.0000944101,0.00003251847],"domain_scores_gemma":[0.9998578,0.00004300326,0.0000457831,0.00001768085,0.00001941137,0.00001633338],"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":[0.00001140962,0.00001492747,0.00004833171,0.00002962595,0.000003588063,0.00003978434,0.000007746942,0.0003770068,0.9972012,0.0001679264,0.00004324583,0.002055144],"study_design_scores_gemma":[0.00001156025,0.0001315797,0.0004926068,0.000006569879,0.00001032544,0.0001850857,0.00001119099,0.005876192,0.9901958,0.00009457041,0.002966954,0.00001766049],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7875547,0.003712935,0.1989141,0.0003947488,0.0002158915,0.0003797182,0.0005877325,0.002303875,0.005936253],"genre_scores_gemma":[0.7107184,0.003493969,0.2795368,0.0002320622,0.00007804008,0.0003697152,0.0005717019,0.0001497853,0.004849555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006106409,"threshold_uncertainty_score":0.002435505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02375947643509717,"score_gpt":0.2431315658054537,"score_spread":0.2193720893703565,"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."}}