{"id":"W4392058550","doi":"10.1002/adsr.202300167","title":"Light‐Up Sensing Citrate Using a Capture‐Selected DNA Aptamer","year":2024,"lang":"en","type":"article","venue":"Advanced Sensor Research","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Waterloo; China Sponsorship Council","keywords":"Aptamer; DNA; Chemistry; Computational biology; Computer science; Molecular biology; Biology; Biochemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005794747,0.0002919457,0.000267257,0.0003501207,0.0003131896,0.0001627866,0.0001984307,0.0002653231,0.000005898156],"category_scores_gemma":[0.0002981433,0.0002510934,0.000166928,0.001356059,0.0001970962,0.00001683305,0.0001568009,0.0005867591,0.00002666197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009335452,"about_ca_system_score_gemma":0.0001864481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005635022,"about_ca_topic_score_gemma":0.00006187988,"domain_scores_codex":[0.9971138,0.0002935317,0.0003158296,0.0009298114,0.0005115069,0.0008355612],"domain_scores_gemma":[0.9984275,0.00006844024,0.00004788003,0.0005947917,0.0006918684,0.000169581],"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":[0.0001037978,0.00001969269,0.000009966785,0.00003826199,0.000096919,0.0001064762,0.00005732829,0.00003316715,0.981801,0.00006046671,0.0005635743,0.01710938],"study_design_scores_gemma":[0.0001765746,0.0001464538,0.00001109315,0.0001260021,0.00002878028,0.0001447582,0.0002852053,0.003965911,0.9455109,0.0002555731,0.04900946,0.000339295],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830188,0.003072012,0.01052788,0.0005658302,0.0002820249,0.000423885,0.00002227009,0.0003090647,0.001778234],"genre_scores_gemma":[0.973556,0.0005586102,0.02218599,0.0001047892,0.0003767729,0.000005200909,0.00006781647,0.00008665397,0.00305812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04844589,"threshold_uncertainty_score":0.9999942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04143218138324663,"score_gpt":0.3823639486515067,"score_spread":0.3409317672682601,"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."}}