{"id":"W2981307444","doi":"","title":"Modifying MACE to accommodate genomic preselection effects","year":2019,"lang":"en","type":"article","venue":"Jukuri (Luonnonvarakeskus Tietopalvelu)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Dairy Commission","funders":"","keywords":"Mace; Sire; Genomic information; Biology; Computer science; Computational biology; Genetics; Genome; Medicine; Gene; Animal science; Internal medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01235105,0.001110279,0.0008757476,0.002100392,0.0006336062,0.002285803,0.001545702,0.0008979694,0.01078356],"category_scores_gemma":[0.06159465,0.0007768135,0.001558092,0.001181949,0.0009897759,0.00354346,0.002870003,0.001754377,0.003452337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004542,"about_ca_system_score_gemma":0.001680109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001782496,"about_ca_topic_score_gemma":0.001656494,"domain_scores_codex":[0.9946193,0.002153668,0.0006275117,0.0009971074,0.00127848,0.0003240273],"domain_scores_gemma":[0.9494215,0.02911561,0.003041034,0.01063552,0.007284626,0.0005017153],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001263707,0.0004034326,0.01962784,0.001262973,0.0006054981,0.00189109,0.0008484562,0.1359257,0.05498721,0.1395586,0.01719365,0.6264319],"study_design_scores_gemma":[0.0002343427,0.0004855032,0.007502977,0.0002911518,0.000285307,0.001945132,0.0001525224,0.6644995,0.08636034,0.1451902,0.09284205,0.0002110039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01173663,0.0001494172,0.9796127,0.0001946175,0.0001003095,0.0001872672,0.0005792044,0.003319134,0.004120651],"genre_scores_gemma":[0.1387271,0.0001569577,0.8532817,0.0002419775,0.0000894233,0.0003280764,0.001077604,0.00163845,0.004458659],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.987649,"threshold_uncertainty_score":0.06531936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006214291375078476,"score_gpt":0.2300134643182752,"score_spread":0.2237991729431967,"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."}}