{"id":"W2024645518","doi":"10.3168/jds.2013-7368","title":"Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe, North America, and Australasia using 2 reference populations","year":2014,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FP7 Food, Agriculture and Fisheries, Biotechnology; National Institute of Food and Agriculture; Instituto Colombiano de Bienestar Familiar; DairyNZ; Bundesministerium für Bildung und Forschung; Aarhus Universitet; European Commission; Gardiner Foundation; University of Alberta; Scotland’s Rural College; Department of Environment and Primary Industries; Kaohsiung Municipal Siaogang Hospital; Department for Environment, Food and Rural Affairs, UK Government; U.S. Department of Agriculture; Scottish Government; Iowa State University","keywords":"Herd; Imputation (statistics); Biology; Genotype; Statistics; Animal science; Genetics; Mathematics; Missing data; Gene","routes":{"ca_aff":false,"ca_fund":true,"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":[0.00950485,0.0009261086,0.001964791,0.001826904,0.0008654461,0.001327694,0.001334761,0.001207,0.001391379],"category_scores_gemma":[0.01528494,0.0007928575,0.001622208,0.004031783,0.0006150877,0.0005547723,0.001266543,0.001246893,0.000506903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006612666,"about_ca_system_score_gemma":0.0007653608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01760614,"about_ca_topic_score_gemma":0.02427535,"domain_scores_codex":[0.9931281,0.003048578,0.0006854968,0.002085343,0.0006421812,0.0004103963],"domain_scores_gemma":[0.9947018,0.001704495,0.0007092692,0.0016469,0.001103621,0.0001339192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003404396,0.0007581846,0.7954476,0.0004359422,0.005417711,0.001415699,0.003216662,0.02975557,0.0249898,0.002078142,0.00640829,0.126672],"study_design_scores_gemma":[0.0004387351,0.0005404798,0.9427611,0.000120148,0.001358843,0.0006658583,0.0005556616,0.03250917,0.005406094,0.003273173,0.01226453,0.0001061411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9066244,0.0007203132,0.0859179,0.0001448591,0.00005905263,0.0002811481,0.004902695,0.0002224035,0.001127376],"genre_scores_gemma":[0.8658379,0.000376504,0.1036476,0.0003013431,0.00002968315,0.0006405672,0.02709104,0.0001451394,0.001930164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01760614,"threshold_uncertainty_score":0.0502671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03252045438889527,"score_gpt":0.3036532135433347,"score_spread":0.2711327591544395,"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."}}