{"id":"W4310396567","doi":"10.3389/fpls.2022.1027558","title":"Integrated model for genomic prediction under additive and non-additive genetic architecture","year":2022,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"ICAR-Indian Agricultural Statistics Research Institute; Indian Agricultural Research Institute; Indian Council of Agricultural Research","keywords":"Genetic architecture; Epistasis; Additive model; Nonparametric statistics; Parametric statistics; Selection (genetic algorithm); Variance (accounting); Additive genetic effects; Computer science; Parametric model; Generalized additive model; Dominance (genetics); Mixed model; Genomic selection; Biology; Statistics; Mathematics; Machine learning; Genotype; Phenotype; Genetics; Gene; Heritability","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":[0.001879347,0.001089287,0.001295145,0.0006546411,0.0003643387,0.001244258,0.001980622,0.001470785,0.002582923],"category_scores_gemma":[0.002711504,0.0004803835,0.001278158,0.0008866818,0.0006485077,0.001034905,0.001076003,0.001977392,0.0007890354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008921717,"about_ca_system_score_gemma":0.001141991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0122511,"about_ca_topic_score_gemma":0.009593759,"domain_scores_codex":[0.9992471,0.00022094,0.00003495698,0.000257486,0.0001380831,0.0001015793],"domain_scores_gemma":[0.9988316,0.0006694894,0.0001272755,0.00006911909,0.0002638783,0.00003866529],"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":[0.00009525214,0.00004503097,0.001914746,0.00003340308,0.00008999484,0.0000727334,0.00005504701,0.9682037,0.001375568,0.004913341,0.0004480009,0.02275318],"study_design_scores_gemma":[0.000003674249,0.00001530959,0.0002811565,0.000002948766,0.00001287754,0.000009816743,0.000002529744,0.9977609,0.000122476,0.001661833,0.0001221003,0.000004459188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04444152,0.0002960413,0.9525962,0.0002203372,0.00003819823,0.00004473959,0.0004453991,0.0006876808,0.00122993],"genre_scores_gemma":[0.8585396,0.0004341776,0.1292371,0.0002178531,0.00007254006,0.0004006373,0.001632442,0.0001467206,0.009318958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0122511,"threshold_uncertainty_score":0.02435958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006569148633078678,"score_gpt":0.2010045699570799,"score_spread":0.1944354213240012,"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."}}