{"id":"W2885929794","doi":"10.1021/acs.analchem.8b02788","title":"Reduction of Background Generated from Template-Template Hybridizations in the Exponential Amplification Reaction","year":2018,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Health; Canada Research Chairs; Canadian Institutes of Health Research; Alberta Innovates; Alberta Innovates - Health Solutions","keywords":"Template; Chemistry; Loop-mediated isothermal amplification; Nucleic acid; Applications of PCR; DNA; Polymerase chain reaction; Multiple displacement amplification; Polymerase; Sequence (biology); Computational biology; Oligonucleotide; Molecular biology; Biophysics; Biochemistry; Nanotechnology; Biology; Digital polymerase chain reaction; Gene; DNA extraction","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":[0.0001643233,0.0001083829,0.0001202422,0.00002655793,0.00007229788,0.00002002127,0.0001341007,0.0001505406,0.00001385938],"category_scores_gemma":[0.00005839092,0.00008761503,0.00007089386,0.000260084,0.0001922802,0.000007318192,0.00002738824,0.00009323361,0.000003338411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002035447,"about_ca_system_score_gemma":0.00003037526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001032736,"about_ca_topic_score_gemma":0.00001997447,"domain_scores_codex":[0.9991006,0.00004492468,0.0002881967,0.0003041696,0.0001344936,0.0001275727],"domain_scores_gemma":[0.9993058,0.00001318693,0.0001223828,0.0003721179,0.0001557284,0.00003080108],"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":[0.00005291248,0.00006728307,0.0002344217,0.000004280405,0.00003378856,7.729982e-7,0.0000136041,0.000004343015,0.998244,0.00001678834,0.001056306,0.0002715175],"study_design_scores_gemma":[0.0001401528,0.00003827556,0.001249554,0.00001166336,0.00005409803,0.00001339313,0.0001554216,0.0004864792,0.9951003,0.0001984229,0.002444613,0.0001076513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945268,0.00002963467,0.004124771,0.0001549172,0.00002991319,0.00005933184,0.0000214587,0.00001589861,0.0010373],"genre_scores_gemma":[0.9975146,0.00005588992,0.0009210345,0.00003832041,0.0004381678,0.000005259906,0.0008249554,0.000009219283,0.0001925597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003203737,"threshold_uncertainty_score":0.3572837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02594772921969745,"score_gpt":0.303004976270564,"score_spread":0.2770572470508665,"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."}}