{"id":"W2148306906","doi":"10.2135/cropsci2011.06.0297","title":"Genomic Selection in Plant Breeding: A Comparison of Models","year":2011,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":689,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hatch; National Institute of Food and Agriculture; Microsoft","keywords":"Biology; Hordeum vulgare; Overfitting; Random forest; Lasso (programming language); Genomic selection; Bayesian probability; Selection (genetic algorithm); Plant breeding; Artificial intelligence; Regression; Linear model; Machine learning; Statistics; Computer science; Mathematics; Agronomy; Poaceae; Genetics; Genotype; Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.004312046,0.0004794955,0.0006416793,0.0007929036,0.0002615773,0.0008945406,0.0008832794,0.0006106316,0.001308724],"category_scores_gemma":[0.006235548,0.0002154059,0.0007291131,0.000772808,0.0004533343,0.000692812,0.0004547188,0.000615642,0.0002054225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008820536,"about_ca_system_score_gemma":0.0004958306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008194588,"about_ca_topic_score_gemma":0.003360459,"domain_scores_codex":[0.9990891,0.0006547972,0.00002725796,0.0001126127,0.00007647886,0.00003974537],"domain_scores_gemma":[0.9944847,0.004988289,0.0001838495,0.0001094185,0.0001841996,0.00004946714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001286668,0.00004447654,0.003458413,0.00005800154,0.00008069605,0.00002453622,0.0000385046,0.9672167,0.0002922165,0.006993646,0.0004159417,0.02124819],"study_design_scores_gemma":[0.000009290038,0.0000255032,0.0007297846,0.00000719123,0.00001198085,0.000007094893,0.000006638323,0.9950097,0.00006238998,0.003948419,0.0001775397,0.000004430299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4006859,0.003280217,0.5859085,0.001477974,0.00007907979,0.0001103135,0.000465634,0.0007546782,0.00723757],"genre_scores_gemma":[0.9478558,0.0008166573,0.04923151,0.0001633302,0.00004515237,0.0001109139,0.0002764312,0.00006166144,0.001438572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008194588,"threshold_uncertainty_score":0.02280462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05154902805762601,"score_gpt":0.2715794352707135,"score_spread":0.2200304072130875,"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."}}