{"id":"W4301430932","doi":"10.1186/s12864-022-08899-6","title":"Design and validation of a 63K genome-wide SNP-genotyping platform for caribou/reindeer (Rangifer tarandus)","year":2022,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation; Artificial Insemination Center of Quebec; Quebec Network for Research on Aging; Ministère des Ressources naturelles et des Forêts; University of Calgary; Université Laval","funders":"Ministère des Forêts, de la Faune et des Parcs; Génome Québec; Université du Québec à Rimouski; Genome Canada","keywords":"Biology; Genotyping; SNP genotyping; Single-nucleotide polymorphism; Molecular Inversion Probe; Genetics; SNP; Population; SNP array; Tag SNP; Runs of Homozygosity; Evolutionary biology; Genotype; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.002737636,0.0004834032,0.0006886616,0.0007389914,0.0006172747,0.0007991415,0.0007672361,0.0009236904,0.001462902],"category_scores_gemma":[0.001737268,0.0003701037,0.0007790242,0.0004476379,0.0004025209,0.000344416,0.0009769277,0.0008011932,0.001455658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004073651,"about_ca_system_score_gemma":0.001446652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001996966,"about_ca_topic_score_gemma":0.00499339,"domain_scores_codex":[0.9982375,0.0002209033,0.00009708873,0.0007820313,0.0004658313,0.0001965223],"domain_scores_gemma":[0.9987597,0.0002521992,0.0001956177,0.0001638118,0.0004333526,0.000195207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005291419,0.0002387244,0.02633136,0.0002570246,0.0001730393,0.0003059493,0.0003853621,0.004775501,0.930775,0.0007023295,0.0008406705,0.03468579],"study_design_scores_gemma":[0.0003088596,0.003673941,0.2656919,0.0001511901,0.0007569945,0.00175605,0.0003697197,0.06545383,0.6339278,0.001341875,0.02635116,0.0002166235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8432164,0.0004793453,0.1404621,0.0002538417,0.00009697828,0.002133009,0.008972827,0.001910679,0.002474806],"genre_scores_gemma":[0.5028691,0.000265809,0.4642074,0.0006802121,0.00002933539,0.003104235,0.02419773,0.0003930949,0.004253167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002737636,"threshold_uncertainty_score":0.01447821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03790783953407082,"score_gpt":0.2365804344298651,"score_spread":0.1986725948957943,"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."}}