{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001388965,0.0004881523,0.0005159294,0.00128662,0.0001993085,0.0006111244,0.0003597245,0.0002437097,0.001211344],"category_scores_gemma":[0.001359749,0.00007614509,0.0005518837,0.001183384,0.0001605213,0.0003286952,0.0002678633,0.0003082094,0.0002548596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005526253,"about_ca_system_score_gemma":0.000277231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001638331,"about_ca_topic_score_gemma":0.00327148,"domain_scores_codex":[0.9991263,0.0002659277,0.00007892996,0.0001526702,0.000338865,0.00003733012],"domain_scores_gemma":[0.9990982,0.0003180733,0.0002348065,0.00005336751,0.0002232068,0.00007248524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009350048,0.0005222519,0.6205235,0.0002047896,0.001040492,0.0003507976,0.000137547,0.03999025,0.09803596,0.001716405,0.001094054,0.235449],"study_design_scores_gemma":[0.00005353581,0.003066146,0.8035461,0.00002804275,0.0003072397,0.0007119902,0.0000775611,0.1690057,0.01796888,0.0006451865,0.004502556,0.00008706602],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9429289,0.0003971893,0.05157464,0.00005704988,0.00002468495,0.00008832705,0.0008226237,0.0002353309,0.003871353],"genre_scores_gemma":[0.9719899,0.00008785584,0.02541858,0.00002604529,0.00002087567,0.0000662056,0.0008565468,0.00003344913,0.001500612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001638331,"threshold_uncertainty_score":0.007345676,"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."}}