{"id":"W2891760220","doi":"10.1111/eva.12713","title":"Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load","year":2018,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"European Research Council; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Inbreeding depression; Inbreeding; Biology; Genetic load; Population; Pedigree chart; Best linear unbiased prediction; Statistics; Evolutionary biology; Population genetics; Effective population size; Population fragmentation; Genetics; Genetic variation; Selection (genetic algorithm); Mathematics; Demography; Computer 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001764864,0.0001143002,0.0001092088,0.00006803132,0.0001857514,0.000008483611,0.0001532934,0.0001083762,0.000009843508],"category_scores_gemma":[0.0000640513,0.0001250052,0.0000498992,0.0001858054,0.0002269341,0.00001220298,0.0000978108,0.00004490817,0.000003065833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003210542,"about_ca_system_score_gemma":0.0001030872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001001055,"about_ca_topic_score_gemma":0.000002003224,"domain_scores_codex":[0.999116,0.00001572077,0.0002473852,0.0002992419,0.0001541321,0.0001675019],"domain_scores_gemma":[0.9992328,0.00004639003,0.0001183617,0.0002375791,0.0002972405,0.00006761844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005300892,0.0009462332,0.004098922,0.0004806167,0.0003797822,1.252477e-7,0.0008127565,0.06038567,0.2802757,0.5579515,0.01334065,0.08079803],"study_design_scores_gemma":[0.006658452,0.005134443,0.1215888,0.0002159874,0.0006330828,0.0001100136,0.0009219947,0.3282662,0.08590568,0.3977412,0.05076358,0.002060553],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1448998,0.0008690109,0.8520156,0.0001076569,0.00007491014,0.000555693,0.000109263,0.00001505852,0.001353031],"genre_scores_gemma":[0.8144417,0.00003211426,0.1846812,0.00003271736,0.0002915534,0.0002322098,0.0001249262,0.00001293566,0.0001505835],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.669542,"threshold_uncertainty_score":0.5097562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03690988347855055,"score_gpt":0.2963045714787884,"score_spread":0.2593946880002378,"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."}}