{"id":"W2472349747","doi":"","title":"Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Canadian Durum Wheat Breeding Lines","year":2015,"lang":"en","type":"article","venue":"Plant and Animal Genome XXIII Conference","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Genomic selection; Training (meteorology); Biology; Population; Biotechnology; Geography; Computer science; Genetics; Artificial intelligence; Demography; Genotype; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0114484,0.000481548,0.0006379353,0.0006903921,0.001024763,0.0008699715,0.001928183,0.0007586845,0.001976402],"category_scores_gemma":[0.02213972,0.0004829966,0.0005331626,0.0005661629,0.000774929,0.0005129757,0.001030906,0.001095006,0.0002842647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002320047,"about_ca_system_score_gemma":0.003387751,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09078272,"about_ca_topic_score_gemma":0.1312348,"domain_scores_codex":[0.9963053,0.001927036,0.0002187577,0.0006971572,0.0005263533,0.0003252754],"domain_scores_gemma":[0.9847577,0.01046656,0.0004526673,0.001151734,0.002887086,0.0002842293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002560182,0.0005090595,0.1224371,0.0001730915,0.0005442325,0.0001582023,0.001312863,0.2892254,0.0714352,0.009547396,0.002316538,0.4997806],"study_design_scores_gemma":[0.0004549257,0.0005355497,0.09333622,0.00003996181,0.0003919421,0.0001028509,0.0002334949,0.8724269,0.026273,0.003604206,0.002529557,0.00007145723],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5772358,0.0001800659,0.4178207,0.0002715516,0.00003665149,0.0003693258,0.000494569,0.001105599,0.002485672],"genre_scores_gemma":[0.8168099,0.00005080386,0.1789336,0.0001789924,0.00001621976,0.0003400639,0.001230745,0.0002634482,0.002176126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9092173,"threshold_uncertainty_score":0.1805086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1573662803037835,"score_gpt":0.2734093593528833,"score_spread":0.1160430790490998,"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."}}