{"id":"W3008426938","doi":"10.1371/annotation/f92c4ed2-b4f7-47aa-97b6-75bce39dcb0e","title":"Correction: Genomic DNA Enrichment Using Sequence Capture Microarrays: a Novel Approach to Discover Sequence Nucleotide Polymorphisms (SNP) in Brassica napus L","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Chromosomal and Genetic Variations","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Plant Biotechnology Institute; National Research Council Canada; Agriculture and Agri-Food Canada","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria","keywords":"Genetics; DNA microarray; Single-nucleotide polymorphism; Brassica; Biology; Computational biology; SNP; SNP array; Sequence (biology); DNA sequencing; genomic DNA; DNA; Bioinformatics; Gene; Genotype","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":[0.00556861,0.002427121,0.002195567,0.004128105,0.002452983,0.002860987,0.003738813,0.00401309,0.1374427],"category_scores_gemma":[0.07387087,0.0009885281,0.001911027,0.004009746,0.001523272,0.002193375,0.001995504,0.00517497,0.04365582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001635847,"about_ca_system_score_gemma":0.003060526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004312677,"about_ca_topic_score_gemma":0.006261126,"domain_scores_codex":[0.9926324,0.001762726,0.001147275,0.001605606,0.002215821,0.0006360564],"domain_scores_gemma":[0.97021,0.01115956,0.001879767,0.003828753,0.01156713,0.001354869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006289492,0.00003622768,0.003053777,0.001873986,0.0003717165,0.001559683,0.0003449945,0.000530572,0.004774692,0.002858312,0.9359435,0.04802367],"study_design_scores_gemma":[0.0002206524,0.0001476928,0.01399559,0.0008088239,0.00029474,0.003611347,0.0004305702,0.005016683,0.007544253,0.007390521,0.9603859,0.0001531687],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.009729302,0.004024229,0.04283663,0.03841048,0.8608554,0.0002992464,0.02062617,0.01144163,0.01177695],"genre_scores_gemma":[0.2764299,0.006580117,0.1870971,0.04763006,0.1050369,0.00165084,0.02788903,0.02279805,0.324888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1374427,"threshold_uncertainty_score":0.4597915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07382676647558119,"score_gpt":0.2257825949320862,"score_spread":0.151955828456505,"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."}}