{"id":"W2394672622","doi":"","title":"A Method for Accuracy of Genetic Evaluation by Utilization of Canadian Genetic Evaluation Information to Improve Heilongjiang Holstein Herds","year":2004,"lang":"en","type":"article","venue":"东北农业大学学报(英文版)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sire; Best linear unbiased prediction; Herd; Population; Dairy cattle; Animal breeding; Trait; Animal science; Biology; Restricted maximum likelihood; Biotechnology; Statistics; Selection (genetic algorithm); Mathematics; Demography; Computer science; Maximum likelihood","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007548756,0.000198666,0.0002094886,0.0002617378,0.0000770183,0.00002157081,0.0002335103,0.0002236791,0.00004064428],"category_scores_gemma":[0.0005830295,0.0002134933,0.00009573079,0.0003540781,0.00004160614,0.00002083995,0.00003830238,0.00005402407,0.000006188639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054186,"about_ca_system_score_gemma":0.0009262063,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008093965,"about_ca_topic_score_gemma":0.006798634,"domain_scores_codex":[0.9982156,0.0001338537,0.0006081872,0.0003142778,0.0004372332,0.0002908964],"domain_scores_gemma":[0.9981379,0.00003136527,0.0003278846,0.000427198,0.0009052402,0.0001704166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001987209,0.0001182348,0.0005228674,0.0002032902,0.0001296041,6.06211e-8,0.001989448,0.1014856,0.4062623,0.0008619917,0.002310817,0.4859171],"study_design_scores_gemma":[0.006144396,0.00306508,0.1341611,0.0001170445,0.0005460688,0.00001047463,0.001117037,0.01402296,0.8119543,0.005749907,0.02233174,0.0007799182],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6396626,0.0007446186,0.3560761,0.0001698762,0.0002237481,0.002222711,0.0002466569,0.000006810999,0.0006468229],"genre_scores_gemma":[0.8209957,0.00002596035,0.1776952,0.0001799985,0.00009040262,0.0002962412,0.0006578031,0.00002166817,0.00003697189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4851372,"threshold_uncertainty_score":0.9985112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02851473902771,"score_gpt":0.3242405047327557,"score_spread":0.2957257657050457,"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."}}