{"id":"W2954126010","doi":"10.3168/jds.2019-16265","title":"Invited review: Advances and applications of random regression models: From quantitative genetics to genomics","year":2019,"lang":"en","type":"review","venue":"Journal of Dairy Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Biology; Selection (genetic algorithm); Regression; Genomics; Computational biology; Quantitative trait locus; Genomic selection; Phenotypic trait; Evolutionary biology; Genetics; Phenotype; Statistics; Genome; Single-nucleotide polymorphism; Computer science; Machine learning; Gene; Genotype; Mathematics","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":[0.005533338,0.00186775,0.003198232,0.002583905,0.0004044414,0.002668679,0.002630015,0.004617341,0.01095012],"category_scores_gemma":[0.01746362,0.0006369517,0.001954397,0.003634694,0.001392447,0.002809889,0.001264678,0.005533986,0.00674079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001652589,"about_ca_system_score_gemma":0.002429492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002544727,"about_ca_topic_score_gemma":0.001590971,"domain_scores_codex":[0.9980982,0.000625379,0.000179641,0.0005298182,0.0004653525,0.0001015723],"domain_scores_gemma":[0.9849234,0.009864417,0.0007685142,0.0003916086,0.003526493,0.0005257301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002057484,0.00006156935,0.0008442932,0.01163021,0.0004120952,0.0002785405,0.0001237397,0.002137701,0.0009939983,0.01151037,0.4627912,0.5090104],"study_design_scores_gemma":[0.00004878919,0.00009213726,0.001745309,0.002754926,0.0002621452,0.0005240809,0.00008301932,0.00166719,0.0003340215,0.01512188,0.9772484,0.0001181222],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002374245,0.9474372,0.006254874,0.0174485,0.02657024,0.0000221525,0.0002145408,0.0001413101,0.001673787],"genre_scores_gemma":[0.003361555,0.9313379,0.003375045,0.009900623,0.04755322,0.00006070389,0.0004950622,0.0001533216,0.003762616],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01095012,"threshold_uncertainty_score":0.03663182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04442058411816724,"score_gpt":0.3520863318337381,"score_spread":0.3076657477155708,"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."}}