{"id":"W2043695363","doi":"10.2135/cropsci2008.08.0512","title":"Genomic Selection for Crop Improvement","year":2009,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1683,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hatch; U.S. Department of Agriculture","keywords":"Biology; Quantitative trait locus; Genomic selection; Selection (genetic algorithm); Heritability; Marker-assisted selection; Trait; Population; Genetics; Biotechnology; Computational biology; Evolutionary biology; Machine learning; Computer science; Gene; Single-nucleotide polymorphism; Genotype","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":[0.001314397,0.0006143824,0.0005068997,0.0007585139,0.0003603923,0.0007926554,0.0006717389,0.0006726778,0.01030982],"category_scores_gemma":[0.001396638,0.0001607824,0.0005450137,0.001323007,0.000551775,0.0005315593,0.000813905,0.001009557,0.001753888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007557051,"about_ca_system_score_gemma":0.0006027771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007363583,"about_ca_topic_score_gemma":0.0009129964,"domain_scores_codex":[0.9993833,0.0002911053,0.00002151213,0.0001587242,0.0001077418,0.00003752572],"domain_scores_gemma":[0.9995211,0.0002127134,0.0001008664,0.00006437422,0.00006656505,0.00003430736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004890237,0.0003474633,0.01181142,0.001049739,0.0003995862,0.0008351833,0.0001746449,0.01915439,0.100408,0.1004081,0.01685093,0.7480716],"study_design_scores_gemma":[0.0007051257,0.002303255,0.06535111,0.0009997432,0.0009553123,0.00274344,0.0004573242,0.05488336,0.05849553,0.2466018,0.5662622,0.0002418552],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.1718128,0.04276142,0.6560186,0.01959431,0.00183245,0.0007131618,0.003168086,0.002541354,0.1015579],"genre_scores_gemma":[0.695601,0.02076684,0.2560361,0.004981883,0.0007203699,0.0005203446,0.003209594,0.0002996055,0.01786434],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01030982,"threshold_uncertainty_score":0.03448975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01004708368862276,"score_gpt":0.2594456901844193,"score_spread":0.2493986064957965,"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."}}