{"id":"W4405178333","doi":"10.1021/jacsau.4c00890","title":"Quantitative Characterization of Partitioning Stringency in SELEX","year":2024,"lang":"en","type":"article","venue":"JACS Au","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Systematic evolution of ligands by exponential enrichment; Aptamer; Selection (genetic algorithm); Biology; Computer science; Genetics; Artificial intelligence; RNA","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":[0.00009781671,0.00005186653,0.0000685644,0.00005658212,0.00001504873,0.000008688306,0.00003243996,0.00005186988,0.000001591669],"category_scores_gemma":[0.00002786584,0.00004676528,0.00003490796,0.0001571119,0.0000269102,0.000003766786,0.00001813843,0.00003628128,0.000001320711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001289914,"about_ca_system_score_gemma":0.00003375988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001539059,"about_ca_topic_score_gemma":0.0000449515,"domain_scores_codex":[0.9995843,0.00002009261,0.0001313516,0.00014022,0.00004620696,0.00007783915],"domain_scores_gemma":[0.9998379,0.000005168225,0.00003290294,0.00007913456,0.00003267529,0.00001221363],"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":[0.0000109121,0.00001483689,0.001939986,0.00001912928,0.00001377357,0.000001652794,0.00004795309,0.000004589077,0.9938631,0.0005476785,0.0000163563,0.003520053],"study_design_scores_gemma":[0.00003929226,0.0001062447,0.007751331,0.00006466073,0.000008358579,0.000001433497,0.00003154697,0.0005274115,0.9891757,0.0001407476,0.002085438,0.00006781932],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931793,0.0001129729,0.006175081,0.00006211971,0.00003836476,0.00004335876,0.00001423155,0.00001758909,0.0003570053],"genre_scores_gemma":[0.9979177,0.0001418228,0.001594869,0.00001675092,0.00004736286,0.000003558644,0.0001361851,0.000006296333,0.0001355267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005811345,"threshold_uncertainty_score":0.1907033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415996772227444,"score_gpt":0.3023071887988086,"score_spread":0.2881472210765341,"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."}}