{"id":"W2019862819","doi":"10.1111/j.1365-2052.2006.01512.x","title":"Marker‐assisted selection based on a multi‐trait economic index in chicken: experimental results and simulation","year":2006,"lang":"en","type":"article","venue":"Animal Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"","keywords":"Sire; Biology; Trait; Selection (genetic algorithm); Heritability; Quantitative trait locus; Marker-assisted selection; Statistics; Value (mathematics); Genetics; Index selection; Index (typography); Evolutionary biology; Gene; Mathematics; Computer science; Artificial intelligence; Animal science","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":[0.002028787,0.0004033589,0.000724795,0.0004034673,0.0002565325,0.0003581864,0.0006094745,0.0005170602,0.0005208307],"category_scores_gemma":[0.002524724,0.0002571089,0.0004629075,0.0004357114,0.0005256006,0.0002815424,0.0002707258,0.000421238,0.00006782352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005666115,"about_ca_system_score_gemma":0.0003014015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008290422,"about_ca_topic_score_gemma":0.005197939,"domain_scores_codex":[0.9996361,0.0002314305,0.0000201659,0.00004003857,0.00003813489,0.00003416326],"domain_scores_gemma":[0.9961004,0.003177326,0.0001693566,0.0002138195,0.0002541034,0.00008501753],"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":[0.0009070502,0.0006876715,0.01380025,0.0001287429,0.0001724623,0.0001472358,0.0001260877,0.9556125,0.01803241,0.00130262,0.0001982435,0.008884791],"study_design_scores_gemma":[0.0001144111,0.0003743032,0.002930235,0.000005576516,0.00004291069,0.00002244562,0.00001582582,0.9910486,0.00505865,0.0002830006,0.00009018427,0.00001375653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819309,0.00007194791,0.01721079,0.00003904238,0.000005770175,0.00003550854,0.00007198991,0.00004492015,0.0005892477],"genre_scores_gemma":[0.9811395,0.00006380036,0.01815676,0.000009091947,0.000002558414,0.0001187611,0.0001210296,0.00001014882,0.0003783832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008290422,"threshold_uncertainty_score":0.01648432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273474935080859,"score_gpt":0.2595947160961178,"score_spread":0.2468599667453092,"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."}}