{"id":"W4367601632","doi":"10.1002/tpg2.20332","title":"Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat ( <i>Triticum aestivum</i> L.)","year":2023,"lang":"en","type":"article","venue":"The Plant Genome","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Biology; Micronutrient; Best linear unbiased prediction; Linear regression; Statistics; Bayesian probability; Regression; Regression analysis; Bayes' theorem; Genomic selection; Selection (genetic algorithm); Population; Biotechnology; Agronomy; Mathematics; Genetics; Genotype; Machine learning; Computer science; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001369239,0.00009847193,0.0001349902,0.00002977172,0.0001133686,0.00001575173,0.0001944628,0.00006500279,0.00001940052],"category_scores_gemma":[0.00001440673,0.00003895467,0.00005383524,0.0002489954,0.00002205008,0.00003968918,0.00003423788,0.00005953422,0.000006603078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004241564,"about_ca_system_score_gemma":0.00001593144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001416823,"about_ca_topic_score_gemma":0.001269397,"domain_scores_codex":[0.9989795,0.00006315917,0.0002333671,0.0001924537,0.0002853713,0.0002461453],"domain_scores_gemma":[0.9995871,0.0001883733,0.00006600147,0.00004284032,0.00007732904,0.0000383092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005894807,0.00004345148,0.0008853463,0.00001668388,0.00001715559,9.777517e-7,0.0003185576,0.01895366,0.9614189,0.0002144974,0.0002429829,0.01782888],"study_design_scores_gemma":[0.000840956,0.0004108545,0.4564135,0.00007736127,0.0001449425,0.00003423922,0.0002630898,0.5112857,0.004347777,0.0226914,0.003220772,0.0002694114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950678,0.000451808,0.0001697173,0.0007293823,0.0001064172,0.0007576683,0.002504623,0.00002343888,0.000189124],"genre_scores_gemma":[0.9989513,0.0001318187,0.00003085945,0.0000263308,0.0001530648,0.00006668441,0.000615285,0.000001377848,0.00002331017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9570711,"threshold_uncertainty_score":0.1588525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08265247910963577,"score_gpt":0.2240127875552299,"score_spread":0.1413603084455941,"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."}}