{"id":"W3092180820","doi":"10.3168/jds.2020-18662","title":"Genomic predictions based on haplotypes fitted as pseudo-SNP for milk production and udder type traits and SCS in French dairy goats","year":2020,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Aptose Biosciences (Canada)","funders":"Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Région Occitanie Pyrénées-Méditerranée; Institut National de la Recherche Agronomique; Agence Nationale de la Recherche","keywords":"Linkage disequilibrium; Haplotype; SNP; Biology; Udder; Population; Genetics; Tag SNP; SNP genotyping; Context (archaeology); Single-nucleotide polymorphism; Genotype; Gene; Medicine; Mastitis","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":[0.00204277,0.0004251622,0.000288841,0.0007306973,0.0001629998,0.0005760525,0.0002983721,0.0002952041,0.0009251715],"category_scores_gemma":[0.003781281,0.0001390416,0.0005054409,0.0003379883,0.0002401155,0.000263089,0.0004133284,0.0002022347,0.0003000694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000318481,"about_ca_system_score_gemma":0.0002729992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008105729,"about_ca_topic_score_gemma":0.008969664,"domain_scores_codex":[0.9993248,0.0004122889,0.00001872814,0.0001638279,0.00004329918,0.00003713981],"domain_scores_gemma":[0.9979451,0.001593734,0.0001437957,0.0001268114,0.0001308536,0.00005959035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008727612,0.0000558327,0.7724845,0.00009769382,0.0006160911,0.000294452,0.0004160809,0.1140895,0.008598255,0.0004424451,0.0007504618,0.1012821],"study_design_scores_gemma":[0.00004818301,0.0002155956,0.5926172,0.00005014121,0.0002285033,0.0002892175,0.0002887404,0.3999542,0.003872347,0.001275028,0.001113876,0.00004718271],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799362,0.0002992225,0.01854176,0.00005441364,0.000006510308,0.000006347415,0.0006243545,0.0001816767,0.0003493568],"genre_scores_gemma":[0.9904297,0.0001024112,0.007423565,0.00002466385,0.000009846614,0.00001480583,0.001694656,0.00003680669,0.000263652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008105729,"threshold_uncertainty_score":0.0161171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0203300637832782,"score_gpt":0.2583019706850258,"score_spread":0.2379719069017475,"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."}}