{"id":"W2605214097","doi":"10.1007/s13353-017-0395-4","title":"Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits","year":2017,"lang":"en","type":"article","venue":"Journal of Applied Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Biology; Residual feed intake; Quantitative trait locus; Genetics; Genome-wide association study; Single-nucleotide polymorphism; Genomics; Genetic architecture; Computational biology; Genotype; Genome; Feed conversion ratio; Body weight; Gene","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":[0.0001520411,0.0001633864,0.0003133994,0.00005686811,0.0001602654,0.00003056827,0.0007095677,0.0001137318,0.000008447082],"category_scores_gemma":[0.00002636565,0.0001233519,0.0001568795,0.00004705271,0.0001774474,0.000003562791,0.0001667403,0.00009424546,7.319589e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000162187,"about_ca_system_score_gemma":0.0001502039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001447234,"about_ca_topic_score_gemma":0.00001186508,"domain_scores_codex":[0.9987577,0.00003852807,0.0005211611,0.0001624657,0.0003341041,0.0001860626],"domain_scores_gemma":[0.9983388,0.00003127713,0.0007864349,0.0004644,0.0002753459,0.0001037402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004117652,0.0002081691,0.001136136,0.0001979868,0.0004611885,7.049683e-7,0.0009814837,0.2446151,0.734633,0.01107442,0.0004773858,0.005802611],"study_design_scores_gemma":[0.002960077,0.001409367,0.3157027,0.0001891727,0.0009202415,0.00008146992,0.001320911,0.006994043,0.661867,0.007451087,0.000484294,0.000619614],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6900613,0.0002094636,0.3083945,0.00003455618,0.0001551748,0.0001884376,0.00004168785,0.000001206082,0.0009135886],"genre_scores_gemma":[0.9423269,0.00002871738,0.05730037,0.00003422451,0.0002591214,0.000003533316,0.000002931327,0.00002236271,0.00002181086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3145666,"threshold_uncertainty_score":0.5030146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06362947823526939,"score_gpt":0.2782596956237678,"score_spread":0.2146302173884985,"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."}}