{"id":"W2899746087","doi":"10.1039/c8ra06675b","title":"Universally applicable, quantitative PCR method utilizing fluorescent nucleobase analogs","year":2018,"lang":"en","type":"article","venue":"RSC Advances","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Primer (cosmetics); TaqMan; Fluorescence; Nucleic acid; Primer dimer; DNA; Nucleobase; SYBR Green I; Taq polymerase; Chemistry; Rolling circle replication; SIGNAL (programming language); Real-time polymerase chain reaction; Polymerase chain reaction; Computational biology; Biochemistry; Biology; DNA polymerase; Thermus aquaticus; Computer science; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00249902,0.001607674,0.00103799,0.001206969,0.0004400573,0.001031432,0.001414873,0.001459311,0.001350472],"category_scores_gemma":[0.002793,0.0009206491,0.0006181464,0.0006594242,0.001046474,0.001058831,0.000934426,0.002080502,0.001683662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005369119,"about_ca_system_score_gemma":0.0009249939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003707695,"about_ca_topic_score_gemma":0.0008037393,"domain_scores_codex":[0.996334,0.0007417131,0.0002294814,0.001343321,0.001134894,0.0002166436],"domain_scores_gemma":[0.998533,0.0005047948,0.0002784109,0.0001795035,0.0004224286,0.00008182231],"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":[0.00004877635,0.00006487503,0.0004190407,0.000439131,0.00001740781,0.0000767815,0.00007800566,0.0004592677,0.9776698,0.001181897,0.0005436086,0.01900148],"study_design_scores_gemma":[0.00001539568,0.0002007131,0.0005341343,0.0000384472,0.00004577538,0.000298561,0.00004091419,0.00717028,0.9773428,0.0003968602,0.01385662,0.00005950465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03529081,0.002727136,0.9545885,0.0001924515,0.00036103,0.000558918,0.0006334601,0.002348738,0.003298856],"genre_scores_gemma":[0.1619875,0.00396737,0.8197677,0.0005015697,0.0001947472,0.001841225,0.001564362,0.0002495846,0.009925991],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00249902,"threshold_uncertainty_score":0.0132162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01651619428707971,"score_gpt":0.3354527890800825,"score_spread":0.3189365947930028,"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."}}