{"id":"W3198265148","doi":"10.1101/2021.08.26.457816","title":"Combined use of Oxford Nanopore and Illumina sequencing yields insights into soybean structural variation biology","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Field Crop Research Alliance; Genome Canada; Syngenta Canada; Saskatchewan Pulse Growers; Fonds de recherche du Québec – Nature et technologies; Grain Farmers of Ontario; Génome Québec","keywords":"Nanopore sequencing; Structural variation; Biology; Illumina dye sequencing; Computational biology; Population; Genetics; DNA sequencing; Genome; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001538389,0.0003288143,0.0004211879,0.00004633925,0.0001915651,0.0001807162,0.000240006,0.0005420847,0.00005663265],"category_scores_gemma":[0.0001974895,0.0001775492,0.00009465066,0.0003641593,0.0001297466,0.0001529635,0.0003621888,0.000297457,9.05024e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009215382,"about_ca_system_score_gemma":0.00006523014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004745306,"about_ca_topic_score_gemma":0.00006828282,"domain_scores_codex":[0.9982749,0.000145538,0.0004631201,0.0006701498,0.000186081,0.0002601935],"domain_scores_gemma":[0.9986188,0.0001165561,0.0004439352,0.0002264398,0.0004695952,0.0001246686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001020332,0.00002311959,0.01734007,0.00006752604,0.00005349043,0.000004172114,0.0001053444,0.00001123768,0.9816964,0.0006391167,0.000008773645,0.00004060807],"study_design_scores_gemma":[0.0002203005,0.0002036383,0.7525378,0.0002023701,0.00007000739,1.897088e-8,0.00005690618,0.002034341,0.2435489,0.00005915844,0.0004922946,0.0005742816],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982837,0.0005669397,0.00006763508,0.0001620513,0.0004582606,0.0003305756,0.00005243173,0.00007645705,0.000001931797],"genre_scores_gemma":[0.9973626,0.0003026673,0.001999449,0.00009784516,0.000196313,0.0000257578,0.000007300283,0.000006213945,0.0000019131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7381474,"threshold_uncertainty_score":0.7240245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02242325159732869,"score_gpt":0.2066142730726272,"score_spread":0.1841910214752985,"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."}}