{"id":"W4241655353","doi":"10.3410/f.1024479.287967","title":"Faculty Opinions recommendation of Nonequilibrium capillary electrophoresis of equilibrium mixtures: a universal tool for development of aptamers.","year":2005,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Aptamer; Capillary electrophoresis; Selection (genetic algorithm); DNA; Computer science; Computational biology; Matching (statistics); Systematic evolution of ligands by exponential enrichment; Chemistry; Biological system; Nanotechnology; RNA; Machine learning; Biology; Chromatography; Materials science; Mathematics; Molecular biology; Gene; Biochemistry; Statistics","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":[0.001646955,0.001585693,0.001046737,0.003985411,0.0004932703,0.001750496,0.002168815,0.001373995,0.02414521],"category_scores_gemma":[0.01036103,0.0004420491,0.0009565728,0.005155021,0.0002058864,0.0008631085,0.0009763922,0.001374476,0.02313034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00142409,"about_ca_system_score_gemma":0.002772625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01556478,"about_ca_topic_score_gemma":0.02581847,"domain_scores_codex":[0.9981983,0.0003259453,0.0002495537,0.000497433,0.0005950849,0.0001337173],"domain_scores_gemma":[0.9966196,0.001121993,0.0005394183,0.0005555956,0.0009105873,0.000252937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002719195,0.00006713896,0.00889636,0.003380266,0.0003272749,0.00008174915,0.0000440078,0.001466741,0.0009888614,0.001023271,0.9527278,0.03072469],"study_design_scores_gemma":[0.0002938509,0.00004481885,0.02002336,0.0004733415,0.0001369487,0.0001216517,0.00006165444,0.002568539,0.002647154,0.001141378,0.972441,0.00004637926],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008659631,0.0003232082,0.0005604248,0.0001677067,0.00003022717,0.00005110503,0.9955339,0.0007227669,0.001744694],"genre_scores_gemma":[0.001859905,0.0001708671,0.001609915,0.00007215447,0.000007678888,0.0001360079,0.9952706,0.00005631934,0.0008165977],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02414521,"threshold_uncertainty_score":0.08077377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01860757961644416,"score_gpt":0.3319721225630456,"score_spread":0.3133645429466015,"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."}}