{"id":"W2316978500","doi":"10.15232/s1080-7446(15)31062-7","title":"A Multiple Trait Selection Index Including Feed Efficiency","year":2006,"lang":"en","type":"article","venue":"The Professional Animal Scientist","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Index (typography); Statistics; Selection (genetic algorithm); Trait; Mathematics; Computer science; Artificial intelligence; World Wide Web; Programming language","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":[0.0002793558,0.0001208373,0.00007738118,0.00002834348,0.0006529089,0.00003233589,0.0002860025,0.00008536112,0.00006524294],"category_scores_gemma":[0.00003784644,0.00008041368,0.00006071384,0.0002044304,0.0001954413,0.000004242244,0.0001774987,0.0001210918,0.00001813797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001467659,"about_ca_system_score_gemma":0.00008794443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001277387,"about_ca_topic_score_gemma":0.000221657,"domain_scores_codex":[0.9989277,0.00007373029,0.0001683439,0.0003038145,0.0002473322,0.0002790733],"domain_scores_gemma":[0.9996635,0.00002252718,0.00007262502,0.0001294282,0.0000641498,0.0000477281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002608775,0.0003082078,0.03876021,0.00001301411,0.0000153213,4.06026e-7,0.0001220012,0.001093578,0.9377566,0.01161558,0.008903202,0.001150981],"study_design_scores_gemma":[0.0005176605,0.0002270163,0.9640735,0.00001644973,0.00001063686,0.00002225047,0.00009522205,0.0007435512,0.02254044,0.002486064,0.009084895,0.0001822994],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989412,0.0001344325,0.006688274,0.0003330711,0.0004541252,0.0002175137,0.00001677428,0.00001965988,0.002724193],"genre_scores_gemma":[0.991185,6.041433e-7,0.00194077,0.0001143077,0.0004558455,0.00002294254,0.00003836188,0.00001101822,0.006231143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9253133,"threshold_uncertainty_score":0.5021714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01310331218537577,"score_gpt":0.2652574173650609,"score_spread":0.2521541051796852,"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."}}