{"id":"W2792754721","doi":"10.1186/s12864-018-4453-z","title":"Inbreeding and runs of homozygosity before and after genomic selection in North American Holstein cattle","year":2018,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":262,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; Canadian Dairy Commission; Dairy Farmers of Canada","keywords":"Inbreeding; Runs of Homozygosity; Best linear unbiased prediction; Biology; Selection (genetic algorithm); Genetics; Population; Inbreeding depression; Statistics; Effective population size; Genetic variation; Mathematics; Genotype; Single-nucleotide polymorphism; Demography; Gene; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001297595,0.0001452091,0.0002188768,0.0002958121,0.0003400464,0.0003718749,0.0003538454,0.0002459525,0.0004045015],"category_scores_gemma":[0.002193786,0.0001486369,0.0002729305,0.0002996069,0.0004087416,0.0002365049,0.0002049319,0.0002459644,0.00004558137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005782121,"about_ca_system_score_gemma":0.0002711567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008541261,"about_ca_topic_score_gemma":0.01790917,"domain_scores_codex":[0.9996237,0.0001645063,0.00001492764,0.0001080008,0.00005035274,0.00003850468],"domain_scores_gemma":[0.9989002,0.0006169922,0.0002184007,0.0001004305,0.00009315088,0.00007083842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003737071,0.0001387732,0.9503635,0.00002682684,0.0002923687,0.000455614,0.0006663079,0.03167707,0.007272748,0.0002291596,0.0001597752,0.008344157],"study_design_scores_gemma":[0.00001308912,0.000166161,0.9624001,0.00000917045,0.00008701136,0.0001400827,0.000173051,0.03577432,0.0008118899,0.000278707,0.0001324852,0.0000140321],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999728,0.00001251165,0.0002009157,0.000003864554,3.627358e-7,7.815809e-7,0.00001210311,0.000002681653,0.00003889676],"genre_scores_gemma":[0.9996755,0.00001041965,0.0002037141,0.00000394849,6.597394e-7,0.000002571754,0.0000597524,0.000002048231,0.00004132842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008541261,"threshold_uncertainty_score":0.01698309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007473535404227005,"score_gpt":0.2149261931374284,"score_spread":0.2074526577332014,"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."}}