{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005177745,0.001227773,0.001199456,0.001419398,0.0003928225,0.001446169,0.002467578,0.001755992,0.001573123],"category_scores_gemma":[0.02114453,0.0005991959,0.00145601,0.001242796,0.001134609,0.00234837,0.001110199,0.002384703,0.0007706353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001309753,"about_ca_system_score_gemma":0.0006916872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004543437,"about_ca_topic_score_gemma":0.003226872,"domain_scores_codex":[0.997877,0.001295295,0.00009936262,0.0003430578,0.000294168,0.00009119888],"domain_scores_gemma":[0.9882528,0.009167009,0.001319361,0.0005355395,0.0005920746,0.0001331643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006466014,0.00006656194,0.01320951,0.0003554645,0.0002832217,0.0002990288,0.0003839463,0.7409506,0.002602419,0.2036857,0.002923988,0.03517487],"study_design_scores_gemma":[0.00001033427,0.0000496193,0.002382619,0.00009517057,0.00007099215,0.0001503986,0.00003165324,0.8630889,0.0004454261,0.1308187,0.002811647,0.00004449517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0361138,0.003066892,0.9564945,0.000672752,0.00005038391,0.00005311674,0.0005359492,0.0003294266,0.002683175],"genre_scores_gemma":[0.7259994,0.007612341,0.2561417,0.000848519,0.0003471247,0.001007308,0.001999959,0.000510826,0.00553292],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005177745,"threshold_uncertainty_score":0.02738291,"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."}}