{"id":"W2801432961","doi":"","title":"Inferring phenotypic causal structures among feed efficiency traits in a commercial turkey population (Meleagris gallopavo)","year":2018,"lang":"en","type":"article","venue":"Queensland's institutional digital repository (The University of Queensland)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Heritability; Feed conversion ratio; Residual feed intake; Trait; Biology; Residual; Statistics; Genetic correlation; Population; Selection (genetic algorithm); Biotechnology; Mathematics; Genetic variation; Body weight; Genetics; Computer science; Demography; Algorithm; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001022751,0.0003668359,0.0002137033,0.0008899257,0.0002786017,0.0003255879,0.0002779758,0.0002930229,0.0008038025],"category_scores_gemma":[0.00167065,0.0002604354,0.000501407,0.0003761666,0.0002445026,0.0002140318,0.0002661229,0.0003253862,0.0001087692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004035115,"about_ca_system_score_gemma":0.000296068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01161524,"about_ca_topic_score_gemma":0.02066854,"domain_scores_codex":[0.9996845,0.0001250845,0.00001569478,0.0001379966,0.00001539447,0.00002132599],"domain_scores_gemma":[0.9991665,0.0005290607,0.0001692487,0.00006726316,0.00003750019,0.00003038092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002284581,0.0001373213,0.9480003,0.00004936966,0.0004601469,0.000374473,0.0003206596,0.01699345,0.01177638,0.0005256824,0.0001374046,0.02099647],"study_design_scores_gemma":[0.0000171345,0.0001415859,0.9203459,0.00001997541,0.0002337995,0.0001853543,0.0003123519,0.07642778,0.001188698,0.0008173376,0.0002929464,0.0000170393],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971492,0.00003370837,0.002496184,0.00001288273,8.346609e-7,0.000005343024,0.0001528814,0.00001449764,0.0001344867],"genre_scores_gemma":[0.9960322,0.00002706374,0.003317945,0.000009420709,0.000001153635,0.000009314586,0.0004452993,0.000004494988,0.0001530996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01161524,"threshold_uncertainty_score":0.02309525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007824747309796012,"score_gpt":0.2029971711593817,"score_spread":0.1951724238495856,"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."}}