{"id":"W2343589051","doi":"10.1149/ma2015-01/39/2062","title":"Aptamer-Based Electrochemical Biosensors for Marine Toxins","year":2015,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Institut National de la Recherche Scientifique","funders":"","keywords":"Aptamer; Biosensor; Marine toxin; Dissociation constant; Systematic evolution of ligands by exponential enrichment; Chemistry; Combinatorial chemistry; Environmental chemistry; Chromatography; Biochemistry; Biology; Computational biology; Toxin; Molecular biology; Receptor; RNA; 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":[0.0005097531,0.0008292387,0.0006136523,0.0004533438,0.0001561212,0.0004045494,0.0008771448,0.001279344,0.0009073594],"category_scores_gemma":[0.0006651087,0.0004124801,0.0003607298,0.0004183273,0.000257401,0.0005544302,0.0004963228,0.001001795,0.0008716294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004027539,"about_ca_system_score_gemma":0.0001377053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002295894,"about_ca_topic_score_gemma":0.0005100515,"domain_scores_codex":[0.9991235,0.0001487526,0.00007047202,0.0002658648,0.0003194098,0.0000719107],"domain_scores_gemma":[0.9997402,0.00009292147,0.00005586233,0.00002273987,0.00006183591,0.00002643268],"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":[0.00001105405,0.000009372821,0.0000359849,0.00005014229,0.000004091895,0.00002187029,0.00001102781,0.00006970642,0.9981901,0.00003949701,0.00002802903,0.001529056],"study_design_scores_gemma":[0.000005447178,0.0001027409,0.0004159363,0.000006576676,0.000009406334,0.0002110024,0.00001452779,0.001615585,0.9957349,0.00006288164,0.001813385,0.000007694246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5806369,0.02355464,0.3867676,0.0008783451,0.0005825934,0.0003883759,0.0007648163,0.001775249,0.004651518],"genre_scores_gemma":[0.7652728,0.01040665,0.21052,0.0008619702,0.0001119814,0.0004834509,0.0007604292,0.00007847845,0.01150429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001279344,"threshold_uncertainty_score":0.003035426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01826181250463979,"score_gpt":0.27879044354525,"score_spread":0.2605286310406102,"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."}}