{"id":"W2146916435","doi":"10.1002/chem.201500994","title":"Optimal DNA Templates for Rolling Circle Amplification Revealed by In Vitro Selection","year":2015,"lang":"en","type":"article","venue":"Chemistry - A European Journal","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Rolling circle replication; Amplicon; Template; DNA; Biology; DNA polymerase; Loop-mediated isothermal amplification; Computational biology; Multiple displacement amplification; DNA nanoball sequencing; Polymerase chain reaction; DNA sequencing; Genetics; Molecular biology; Genomic library; Nanotechnology; Base sequence; Gene; DNA extraction; Materials science","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":[0.0007409138,0.0001348887,0.0001356373,0.00002818204,0.0001017741,0.00006233344,0.0001518499,0.00006863096,0.000001113132],"category_scores_gemma":[0.0001968416,0.000136773,0.00009199409,0.0001047183,0.00003998678,0.00000813734,0.00003014307,0.0001699292,0.000001625078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005270562,"about_ca_system_score_gemma":0.00004913703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.191125e-7,"about_ca_topic_score_gemma":2.836612e-7,"domain_scores_codex":[0.9990075,0.00008047563,0.0003113256,0.000276575,0.0001025928,0.0002215969],"domain_scores_gemma":[0.9993267,0.000007878342,0.0001984567,0.0001471837,0.000187479,0.0001322948],"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":[0.0002168578,0.00004347964,0.000124375,0.000009728054,0.00002419296,0.000004330372,0.00001613069,0.00009821555,0.9919653,6.997801e-8,0.003965174,0.003532133],"study_design_scores_gemma":[0.0007023131,0.00009065065,0.00006246601,0.00002164005,0.00002167878,0.0001920144,0.00008150217,0.001044469,0.9824128,0.00001669189,0.01517506,0.0001787485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9657846,0.000278205,0.03337086,0.0001283099,0.00003781821,0.00008441509,0.00001408773,0.00003029165,0.000271419],"genre_scores_gemma":[0.9842234,0.00006041094,0.01448327,0.00005774814,0.0004128526,0.000003908969,0.0002107831,0.00003022711,0.0005174543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01888759,"threshold_uncertainty_score":0.5577442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171224324134701,"score_gpt":0.2646338172660075,"score_spread":0.2475113848525374,"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."}}