{"id":"W1980349612","doi":"10.1021/ac060144h","title":"Selection of Smart Aptamers by Methods of Kinetic Capillary Electrophoresis","year":2006,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Capillary electrophoresis; Aptamer; Selection (genetic algorithm); Chromatography; Kinetic energy; Molecular biology; Artificial intelligence","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":[0.0001496925,0.0001455064,0.0002596135,0.00002689274,0.00002374452,0.000004237986,0.0001124977,0.0002003493,0.00001564732],"category_scores_gemma":[0.0000862251,0.0001338225,0.0001842992,0.0002439557,0.0001975274,0.000001720427,0.00003738709,0.00008931637,2.860185e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001662148,"about_ca_system_score_gemma":0.00003298278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006828415,"about_ca_topic_score_gemma":0.000004481549,"domain_scores_codex":[0.9990105,0.00004059051,0.0003345278,0.0002856661,0.0001368194,0.0001918829],"domain_scores_gemma":[0.9994021,0.00002133425,0.0001502369,0.0002346613,0.000144317,0.00004735442],"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":[0.00005266636,0.0000812263,0.001160411,0.00003815162,0.00007458791,4.776887e-7,5.738678e-7,0.000002896631,0.9936612,0.00001832314,0.004165977,0.0007435158],"study_design_scores_gemma":[0.0001167404,0.0001153967,0.0001830619,0.000009351461,0.0001106861,0.000008090112,0.00000847461,0.0003106808,0.9939848,0.0001482572,0.004863709,0.0001407797],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896908,0.0004614877,0.005976018,0.0000599098,0.000008287086,0.00005020745,0.00001625186,0.00002171935,0.003715289],"genre_scores_gemma":[0.9880111,0.0001626331,0.01052732,0.00002645887,0.00006296905,0.000002079859,0.000113752,0.00001435058,0.00107936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004551301,"threshold_uncertainty_score":0.5457123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0048598346820534,"score_gpt":0.2809152138195232,"score_spread":0.2760553791374697,"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."}}