{"id":"W2339602543","doi":"10.1149/ma2015-01/9/866","title":"Selection, Characterization, and Application of High Affinity Microcystin-Targeting Aptamers in a Graphene-Based Biosensing Platform","year":2015,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Institut National de la Recherche Scientifique","funders":"","keywords":"Aptamer; Biosensor; Graphene; Systematic evolution of ligands by exponential enrichment; Microcystin-LR; Detection limit; Chemistry; Nanotechnology; Materials science; Chromatography; Biology; Biochemistry; Cyanobacteria; Molecular biology; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000433687,0.0001342906,0.000189887,0.0001308052,0.00008239873,0.00003000932,0.00005660432,0.00004839274,0.000002356947],"category_scores_gemma":[0.00005074687,0.0001452463,0.00002856432,0.0003092911,0.00004065767,0.0001227415,0.000019283,0.0001306492,0.000003801073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002830875,"about_ca_system_score_gemma":0.00009101193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001115948,"about_ca_topic_score_gemma":0.00002171703,"domain_scores_codex":[0.9990339,0.0000270018,0.0004110544,0.0002117887,0.0001112824,0.0002049461],"domain_scores_gemma":[0.9992162,0.0000766224,0.0003739069,0.00009071387,0.0001588372,0.00008371246],"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":[0.00004731838,0.000169114,0.1385885,0.00004938433,0.00001871014,6.667432e-7,0.0006165577,0.01685409,0.8393126,0.0001054591,0.00003378227,0.004203749],"study_design_scores_gemma":[0.001067757,0.00004116757,0.09890804,0.0001233882,0.0000253498,0.000001411448,0.0004484636,0.005942069,0.8924561,0.0004116754,0.000308095,0.0002665461],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962376,0.00001001275,0.002269188,0.00005110219,0.00007321696,0.000157178,0.00001165679,0.000028333,0.001161758],"genre_scores_gemma":[0.994256,0.000001260022,0.005460905,0.00001399035,0.0001121348,0.000003965212,0.0001268161,0.00001707575,0.000007812754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05314341,"threshold_uncertainty_score":0.5922973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01171193821509533,"score_gpt":0.2204964238115767,"score_spread":0.2087844855964814,"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."}}