{"id":"W2335625396","doi":"10.1021/ac402220k","title":"Selection and Identification of DNA Aptamers against Okadaic Acid for Biosensing Application","year":2013,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":138,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aptamer; Okadaic acid; Chemistry; Biosensor; Dissociation constant; Detection limit; Systematic evolution of ligands by exponential enrichment; Chromatography; Combinatorial chemistry; Linear range; Marine toxin; Biochemistry; Phosphatase; Toxin; Molecular biology; Enzyme","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":[0.0001072397,0.0001047093,0.0001293647,0.0000226584,0.00005390408,0.00001947666,0.00005667852,0.0001445715,0.000001057846],"category_scores_gemma":[0.0001142354,0.00009967374,0.00007777853,0.0001130022,0.0001035769,0.000005872167,0.00002620856,0.0000462358,8.790991e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000136507,"about_ca_system_score_gemma":0.00001589948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006260133,"about_ca_topic_score_gemma":0.000002300914,"domain_scores_codex":[0.9992313,0.000008837216,0.0002577771,0.0002960526,0.00007637173,0.0001296529],"domain_scores_gemma":[0.9994261,0.00001180368,0.0001333866,0.0001818741,0.0001927887,0.00005401252],"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":[0.00001192916,0.00002341117,0.0002357822,0.0000392194,0.00003609339,3.551592e-8,0.000002864894,0.000002214697,0.9898008,0.00001137432,0.0002506966,0.009585639],"study_design_scores_gemma":[0.0001103251,0.0000262746,0.0002800543,0.000007364711,0.00004731446,0.000003392886,0.00003796273,0.009358576,0.9890846,0.0002478885,0.0006815749,0.0001146471],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793554,0.00005241246,0.02000799,0.0002046287,0.000006672438,0.0001598622,0.000008513369,0.00002360076,0.0001809083],"genre_scores_gemma":[0.9965765,0.00006332428,0.002761707,0.00005133654,0.00008249695,0.00001674487,0.0001399034,0.00001131489,0.000296657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01724629,"threshold_uncertainty_score":0.4064577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006128758371953554,"score_gpt":0.2567648645308462,"score_spread":0.2506361061588927,"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."}}