{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009504022,0.0006307001,0.0004770365,0.0005646386,0.0002555107,0.0009032931,0.0008213487,0.0005258276,0.0006955128],"category_scores_gemma":[0.001240914,0.000345726,0.0002997892,0.0002945045,0.0004666837,0.0005594624,0.0005602794,0.0008838632,0.0007944911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003358179,"about_ca_system_score_gemma":0.0003034116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002394989,"about_ca_topic_score_gemma":0.0002875066,"domain_scores_codex":[0.9990262,0.0001983219,0.00008832227,0.0002273815,0.0003672613,0.00009251965],"domain_scores_gemma":[0.9991999,0.0003674839,0.00009673443,0.00008906309,0.0001897452,0.00005703039],"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":[0.00007180332,0.00004180521,0.000225736,0.0001008571,0.00001152491,0.00006458457,0.00005655031,0.0005482892,0.9708082,0.003744917,0.0003437737,0.0239819],"study_design_scores_gemma":[0.00001280699,0.00006196863,0.0002270338,0.000005341477,0.000009291336,0.000262658,0.00001009296,0.006581012,0.9863832,0.000487151,0.005935696,0.00002373183],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1306964,0.004725044,0.8573642,0.0003642482,0.000217313,0.00032025,0.0002461643,0.001655794,0.004410586],"genre_scores_gemma":[0.4357437,0.004125237,0.5504513,0.0004259772,0.00007863485,0.0006743362,0.0005678183,0.000304495,0.007628539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009504022,"threshold_uncertainty_score":0.005026221,"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."}}