{"id":"W2114638642","doi":"10.1139/g02-063","title":"Genetic diversity among Canadienne, Brown Swiss, Holstein, and Jersey cattle of Canada based on 15 bovine microsatellite markers","year":2002,"lang":"en","type":"article","venue":"Genome","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Région Bretagne; U.S. Department of Agriculture","keywords":"Breed; Biology; Brown Swiss; Genetic diversity; Microsatellite; Loss of heterozygosity; Allele; Holstein Cattle; Genetic variation; Coat; Genotyping; Genetics; Dairy cattle; Genotype; Ecology; Demography; Population; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.0004748416,0.0001706671,0.0002155894,0.00162446,0.001238722,0.0007137046,0.000339442,0.0001670421,0.0009448355],"category_scores_gemma":[0.0005394763,0.0001187335,0.0001804627,0.002050957,0.0005390669,0.0001221871,0.0002976265,0.0001857762,0.0001020302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005385477,"about_ca_system_score_gemma":0.003992379,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9347695,"about_ca_topic_score_gemma":0.9823016,"domain_scores_codex":[0.9995975,0.0000281102,0.00001469961,0.00007814401,0.0001642753,0.0001172686],"domain_scores_gemma":[0.9994553,0.00007351003,0.00005177841,0.00001676187,0.0002575647,0.0001450921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004719459,0.00004649253,0.9571583,0.00005285475,0.0002106191,0.0003381573,0.003281468,0.0004238807,0.01636,0.0004654968,0.0005981491,0.02059269],"study_design_scores_gemma":[0.00000949261,0.00002701278,0.9971848,0.00001312321,0.00003417397,0.0001051116,0.0009646582,0.0003068649,0.0001968866,0.0000300444,0.001120504,0.0000072886],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984548,0.0001363645,0.00009342225,0.00002043153,0.000001199132,0.000006575017,0.0002856355,0.000003662844,0.0009979282],"genre_scores_gemma":[0.9971463,0.0001717625,0.0004703945,0.00002289695,0.000001572797,0.000006274301,0.00098029,0.000003217516,0.001197398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06523055,"threshold_uncertainty_score":0.1312293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006172952579864752,"score_gpt":0.1599545804412536,"score_spread":0.1537816278613889,"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."}}