{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003257313,0.0007730158,0.0005586386,0.0002438526,0.0001951943,0.00084625,0.0007036612,0.0006595743,0.001148149],"category_scores_gemma":[0.0003754028,0.0003130277,0.0003649963,0.000236387,0.0005902582,0.0006508543,0.000660523,0.001062769,0.001066912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004408802,"about_ca_system_score_gemma":0.0002612028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000305669,"about_ca_topic_score_gemma":0.0008357649,"domain_scores_codex":[0.9995053,0.0000562265,0.00002520275,0.000181429,0.0001890324,0.00004267223],"domain_scores_gemma":[0.9997409,0.00008171911,0.0000686775,0.00005404735,0.00002953892,0.00002514214],"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":[0.000007822821,0.00001103298,0.00004059725,0.00004792283,0.000004051075,0.00001330311,0.000008759875,0.0002233511,0.9976792,0.0002575998,0.00006232606,0.001644118],"study_design_scores_gemma":[0.000004806036,0.00004936677,0.000231134,0.000003345763,0.000006287273,0.0000510998,0.00001021882,0.001758012,0.9940299,0.0001347199,0.00371173,0.000009409242],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4339288,0.002733554,0.5504339,0.0005379393,0.0002285082,0.000460688,0.001052182,0.003222733,0.007401696],"genre_scores_gemma":[0.8080063,0.002394649,0.1782846,0.0003501283,0.00005993098,0.0004723851,0.001662215,0.0002590732,0.008510789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001148149,"threshold_uncertainty_score":0.003840864,"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."}}