{"id":"W2055912506","doi":"10.1021/es301686k","title":"Selection, Characterization, and Biosensing Application of High Affinity Congener-Specific Microcystin-Targeting Aptamers","year":2012,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aptamer; Congener; Microcystin; Detection limit; Biosensor; Chemistry; Combinatorial chemistry; Biology; Environmental chemistry; Chromatography; Biochemistry; Molecular biology; Cyanobacteria; Genetics; Bacteria","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":[0.000378017,0.0003399145,0.0002903283,0.0002334386,0.0001614887,0.0002695331,0.0002186459,0.0003369634,0.0003293966],"category_scores_gemma":[0.0005197679,0.0001597591,0.0001505783,0.0001923049,0.0002263871,0.0001658358,0.0001768793,0.0003100596,0.000220688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003048809,"about_ca_system_score_gemma":0.0001858504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006605674,"about_ca_topic_score_gemma":0.001232986,"domain_scores_codex":[0.9996217,0.00006078121,0.00004417258,0.0001083149,0.0001217899,0.00004310354],"domain_scores_gemma":[0.9997211,0.00007407152,0.00006326567,0.0000328305,0.00006954381,0.00003912977],"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":[0.000005561181,0.000005536141,0.0001094025,0.000006833458,0.000001185611,0.00001049538,0.000007206881,0.00005960774,0.9992784,0.00001462822,0.00000371234,0.0004974126],"study_design_scores_gemma":[0.000002036412,0.00004526869,0.0006026499,6.617078e-7,0.000003317044,0.00009099786,0.00000449598,0.0004410571,0.9985148,0.000009785505,0.0002830545,0.000001834508],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9694427,0.0007157569,0.02859726,0.0001088494,0.00001437228,0.0001123011,0.0001660297,0.0001015537,0.0007412526],"genre_scores_gemma":[0.9648877,0.0004954367,0.03280284,0.00005663611,0.000007864723,0.00005781576,0.0002660255,0.00003049019,0.001395362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006605674,"threshold_uncertainty_score":0.002212048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003839888102664158,"score_gpt":0.2109649234123962,"score_spread":0.207125035309732,"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."}}