{"id":"W3170855143","doi":"10.1002/csc2.20583","title":"Improvement of key agronomical traits in soybean through genomic prediction of superior crosses","year":2021,"lang":"en","type":"article","venue":"Crop Science","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Grain Research Centre; Agriculture and Agri-Food Canada","funders":"","keywords":"Biology; Selection (genetic algorithm); Biotechnology; Cultivar; Yield (engineering); Trait; Plant breeding; Maturity (psychological); Key (lock); Genomic selection; Agronomy; Genetics; Genotype; Gene; Computer science; Single-nucleotide polymorphism; Ecology; Machine learning","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":[0.001227083,0.000325308,0.0002500017,0.0003783703,0.0001277289,0.0003174891,0.0001612683,0.0002051882,0.0004451004],"category_scores_gemma":[0.000770062,0.000119246,0.0002255974,0.0002278744,0.0001019242,0.0001905673,0.0002111809,0.0003050681,0.0001353413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003934754,"about_ca_system_score_gemma":0.0002573294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001499849,"about_ca_topic_score_gemma":0.00296824,"domain_scores_codex":[0.9998211,0.0000485991,0.0000150483,0.000058042,0.00004034402,0.00001701067],"domain_scores_gemma":[0.999369,0.0002311292,0.0002373887,0.00003933629,0.00006055948,0.00006260585],"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":[0.0005530496,0.0001160771,0.2042748,0.00005915239,0.00009359718,0.0003108948,0.000120881,0.02233507,0.7458662,0.0003930283,0.000111414,0.0257657],"study_design_scores_gemma":[0.00004865252,0.001529604,0.7440866,0.00003130683,0.0002282385,0.0007170169,0.0001827675,0.1134989,0.1367633,0.0007202919,0.002120828,0.00007250953],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948123,0.0001161622,0.004537762,0.00001782005,0.000002353084,0.000005320518,0.0002427384,0.0000494797,0.0002160467],"genre_scores_gemma":[0.9934697,0.00006682266,0.005663278,0.0000114658,0.000001703177,0.000004208137,0.0005485641,0.00001454693,0.0002198231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001499849,"threshold_uncertainty_score":0.006489515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02520679817288369,"score_gpt":0.23327887409974,"score_spread":0.2080720759268563,"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."}}