{"id":"W4311326440","doi":"10.21203/rs.3.rs-2226166/v1","title":"3D-GBS: A universal genotyping-by-sequencing approach for genomic selection and other high-throughput low-cost applications in species with small to medium-sized genomes","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Government of Canada; Université Laval; Grain Farmers of Ontario; Génome Québec; Canadian Field Crop Research Alliance; Genome Canada","keywords":"Genotyping; Genome; Biology; DNA sequencing; Context (archaeology); Computational biology; Selection (genetic algorithm); Reference genome; Genetics; Genotype; Computer science; Gene; Artificial intelligence","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":[0.002137384,0.0009152191,0.001243846,0.001012053,0.0004014074,0.0008951469,0.001046208,0.0009663721,0.00253478],"category_scores_gemma":[0.001706361,0.0008168895,0.0009762908,0.0007283207,0.0006292033,0.0007070485,0.001538051,0.001742932,0.001958833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003905225,"about_ca_system_score_gemma":0.0007107347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009434853,"about_ca_topic_score_gemma":0.001970731,"domain_scores_codex":[0.9985741,0.0003684026,0.0001221447,0.0004338505,0.0003891838,0.0001123699],"domain_scores_gemma":[0.998958,0.0003065844,0.0002121233,0.0002574157,0.0001682121,0.00009758337],"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":[0.0001654302,0.00004738479,0.0007897666,0.0003051375,0.00006492764,0.0001140489,0.0001338725,0.001972121,0.9467851,0.001389833,0.001511109,0.04672128],"study_design_scores_gemma":[0.00007461508,0.0003501752,0.005976366,0.00009072028,0.0001190661,0.000956567,0.0001194087,0.07019096,0.8725951,0.00315838,0.04621796,0.0001507473],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04786646,0.001079486,0.937448,0.0002338002,0.0001402985,0.0003245695,0.002308868,0.009005293,0.001593092],"genre_scores_gemma":[0.09917229,0.0005416838,0.8924986,0.0003309647,0.00002800241,0.0005182282,0.003039473,0.001548013,0.002322702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00253478,"threshold_uncertainty_score":0.01130366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04940305917724547,"score_gpt":0.2983937777721344,"score_spread":0.2489907185948889,"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."}}