{"id":"W3194385641","doi":"10.3389/fbioe.2021.727584","title":"Decentralizing Cell-Free RNA Sensing With the Use of Low-Cost Cell Extracts","year":2021,"lang":"en","type":"article","venue":"Frontiers in Bioengineering and Biotechnology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Agencia Nacional de Investigación y Desarrollo; Pontificia Universidad Católica de Chile; Comisión Nacional de Investigación Científica y Tecnológica; Danmarks Tekniske Universitet; Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias; International Center for Genetic Engineering and Biotechnology; University of Toronto; University of Minnesota","keywords":"RNA; Computer science; Biosensor; Nanotechnology; Gene; Computational biology; Biology; Materials science; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.00006594363,0.0001617199,0.0001836609,0.00009765488,0.00003485202,0.00001973487,0.0001428276,0.0002542583,7.642104e-7],"category_scores_gemma":[0.00004283591,0.0001318986,0.00003564539,0.0002250618,0.0001092406,0.000004576804,0.0001312264,0.0001928548,1.54379e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001037745,"about_ca_system_score_gemma":0.00002758751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001172799,"about_ca_topic_score_gemma":0.00002347555,"domain_scores_codex":[0.9991882,0.00001529498,0.000157317,0.0002771828,0.00006780037,0.0002941593],"domain_scores_gemma":[0.9994445,0.00001287532,0.00004240031,0.0004265673,0.00003217124,0.00004149475],"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":[0.00002270579,0.00002653039,0.0009179295,0.0000936802,0.00002639129,0.00003165536,0.00004400594,0.01298193,0.9778865,0.00001256833,0.001397473,0.006558627],"study_design_scores_gemma":[0.0004671151,0.00005459576,0.0004952008,0.00004504153,0.0000159773,0.00004526758,0.0002303679,0.004467681,0.9781764,0.000004904133,0.01582033,0.0001771702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.701024,0.005191062,0.2929902,0.0004140629,0.0002111372,0.0001092017,0.000009477057,0.00002619913,0.00002465679],"genre_scores_gemma":[0.9390749,0.00184366,0.05887819,0.00004645674,0.0000323846,0.000002938795,0.00001460883,0.00002432422,0.00008251124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2380509,"threshold_uncertainty_score":0.5378669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006730671212553379,"score_gpt":0.2096810427365262,"score_spread":0.2029503715239729,"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."}}