{"id":"W3007506343","doi":"10.3389/fgene.2020.00067","title":"NanoGBS: A Miniaturized Procedure for GBS Library Preparation","year":2020,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Genome Canada","keywords":"Genotyping; SNP genotyping; Multiplexing; Single-nucleotide polymorphism; SNP; DNA sequencing; Ion semiconductor sequencing; Computational biology; Computer science; Throughput; Biology; Genotype; Genetics; DNA; Operating system; Gene; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"protocol","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004307557,0.0001511501,0.0001736301,0.00002830614,0.00004828128,0.00002369538,0.000188865,0.0001401862,0.000002916586],"category_scores_gemma":[0.00005427412,0.0001566605,0.00007806495,0.00009459181,0.00004206847,0.000001173902,0.00009667537,0.00004771776,0.000001425237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005635001,"about_ca_system_score_gemma":0.00009461522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.167162e-7,"about_ca_topic_score_gemma":0.000002331757,"domain_scores_codex":[0.9991104,0.00002393211,0.0002142349,0.0003666188,0.00006629908,0.0002185372],"domain_scores_gemma":[0.9996491,0.000005812718,0.00006516314,0.0001717655,0.00003012192,0.00007801612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001497905,0.0001139761,0.07645829,0.0002186683,0.0002195842,0.000002643498,0.001612931,0.001429733,0.534619,0.00005258702,0.378653,0.005121724],"study_design_scores_gemma":[0.002477791,0.001383338,0.007905064,0.00001713616,0.00005604488,0.000002895556,0.0004750103,0.00498321,0.3079466,0.0009639366,0.6732216,0.0005672779],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978682,0.01222704,0.005603238,0.001210125,0.0005482964,0.001005356,0.00009858625,0.00001107809,0.0006142332],"genre_scores_gemma":[0.8439655,0.001835984,0.1512133,0.001585562,0.0005996537,0.0001524526,0.0001518664,0.00004917387,0.000446572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2945687,"threshold_uncertainty_score":0.6388431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009611726642234423,"score_gpt":0.2240070096455244,"score_spread":0.21439528300329,"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."}}