{"id":"W4226326187","doi":"10.1007/s12686-022-01259-2","title":"Targeted genome-wide SNP genotyping in feral horses using non-invasive fecal swabs","year":2022,"lang":"en","type":"article","venue":"Conservation Genetics Resources","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Calgary","funders":"Fisheries and Oceans Canada; Faculty of Veterinary Medicine, University of Calgary; Canada Foundation for Innovation; Parks Canada; Natural Sciences and Engineering Research Council of Canada; Government of Alberta; University of Calgary","keywords":"Genotyping; Biology; SNP genotyping; Genotype; Single-nucleotide polymorphism; SNP; Genetics; Genome; Molecular Inversion Probe; Microsatellite; SNP array; Computational biology; Gene; Allele","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":[0.001066371,0.0003275596,0.0003508948,0.0005845273,0.0002797028,0.0004652066,0.0003331657,0.000429609,0.0004500199],"category_scores_gemma":[0.001394578,0.0002121407,0.0002547199,0.0004258393,0.0003355634,0.000144444,0.0002476318,0.0001914567,0.0002682114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002856985,"about_ca_system_score_gemma":0.0002495364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005867607,"about_ca_topic_score_gemma":0.02540893,"domain_scores_codex":[0.998716,0.0002739958,0.00007276415,0.0004777847,0.0003720926,0.00008747917],"domain_scores_gemma":[0.9993343,0.0002001053,0.0001793076,0.0001089059,0.0001440907,0.00003321652],"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":[0.0003132,0.00007273404,0.1474774,0.0000982801,0.0000928548,0.0001799697,0.0005450134,0.001177962,0.8252341,0.00009135081,0.0001919178,0.02452531],"study_design_scores_gemma":[0.0000138146,0.0007411661,0.8369191,0.00002950688,0.0001169038,0.0008011529,0.0002549715,0.004814905,0.1534257,0.0001204262,0.002733204,0.00002914082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986187,0.0002670387,0.01191392,0.00002815746,0.000005208258,0.00006749343,0.000856303,0.00008600887,0.0005888139],"genre_scores_gemma":[0.9662542,0.0002532345,0.03019414,0.00009010481,0.000006983958,0.0001268529,0.00169648,0.00002249231,0.00135554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005867607,"threshold_uncertainty_score":0.01166689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02617902300397276,"score_gpt":0.2468310894802205,"score_spread":0.2206520664762477,"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."}}