{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002652415,0.001425253,0.001215422,0.0004207916,0.0003256733,0.0008946288,0.001202907,0.0009445128,0.001388433],"category_scores_gemma":[0.003489334,0.0007685184,0.0005750358,0.0005104207,0.000846633,0.0007085935,0.0009863465,0.002216514,0.001195228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005685206,"about_ca_system_score_gemma":0.0005839084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003045297,"about_ca_topic_score_gemma":0.0004903392,"domain_scores_codex":[0.996951,0.000789576,0.0001656842,0.0007619981,0.001053471,0.0002782921],"domain_scores_gemma":[0.9976799,0.001214474,0.0002535274,0.0002933546,0.000436117,0.0001225752],"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":[0.00006121313,0.00003455772,0.0001937739,0.00009458217,0.000008518453,0.00006113437,0.00006473965,0.0001415512,0.9957093,0.0003642432,0.00004615835,0.003220121],"study_design_scores_gemma":[0.000004007126,0.00008015858,0.0002639912,0.000007637272,0.000008492427,0.00006224179,0.00001145543,0.001954372,0.9965125,0.0001115776,0.0009767987,0.000006937255],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4502389,0.003310238,0.5390611,0.0003208024,0.0002197144,0.0004218256,0.000220736,0.001696154,0.00451048],"genre_scores_gemma":[0.675678,0.002860624,0.3089314,0.0003840122,0.0000473927,0.0007897316,0.0008212333,0.0004378515,0.01004984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002652415,"threshold_uncertainty_score":0.01402754,"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."}}