{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001986794,0.0003852968,0.0001905254,0.0003191884,0.0001204217,0.0002141922,0.0002659839,0.000380693,0.0002148168],"category_scores_gemma":[0.0003326081,0.0001474904,0.0001534128,0.0002195808,0.0001749464,0.0001491647,0.0001642223,0.0002299649,0.000148933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002334177,"about_ca_system_score_gemma":0.0001436022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008072008,"about_ca_topic_score_gemma":0.001285122,"domain_scores_codex":[0.9997941,0.00002749632,0.00001971559,0.00003698606,0.00009064823,0.00003112359],"domain_scores_gemma":[0.9998654,0.00003076195,0.00003205125,0.00001844822,0.0000304281,0.00002292291],"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":[0.000005257955,0.000004047999,0.00005775976,0.00000878289,9.579037e-7,0.00001673196,0.000006058562,0.00009659047,0.9993346,0.00001550804,0.000004125706,0.0004496171],"study_design_scores_gemma":[0.000001460917,0.00003612132,0.0007589629,9.126382e-7,0.000003456712,0.00005979283,0.000004990395,0.001082152,0.9977489,0.00001052146,0.0002897765,0.000003054864],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793601,0.0006631884,0.01879089,0.00007266839,0.00001451624,0.000129657,0.0002224102,0.0001394406,0.0006071947],"genre_scores_gemma":[0.9719507,0.0004562558,0.0262256,0.00002892286,0.000005088544,0.00006078971,0.0002633164,0.00001615634,0.0009931241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008072008,"threshold_uncertainty_score":0.001693547,"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."}}