{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001844349,0.0001848202,0.0001692813,0.0001597243,0.0004069267,0.00005034942,0.0003014005,0.0001242629,0.0001667452],"category_scores_gemma":[0.00007989993,0.0002259124,0.00008121139,0.0002992977,0.00008066971,0.000005188323,0.000377526,0.0001652569,0.000004474816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000054461,"about_ca_system_score_gemma":0.0001502676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001065375,"about_ca_topic_score_gemma":0.0001543564,"domain_scores_codex":[0.9985694,0.0001666247,0.0003088562,0.0004026909,0.0002661067,0.0002862855],"domain_scores_gemma":[0.9993388,0.00002300588,0.0001601082,0.0002919358,0.0001051543,0.00008098951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001229132,0.00003460727,0.479644,0.00002346658,0.00004829131,0.00001229986,0.001128004,0.1429946,0.3750963,0.000008905591,0.0006439177,0.0002426927],"study_design_scores_gemma":[0.001436959,0.0002609248,0.8062209,0.00001316913,0.0000487063,0.00003810088,0.001720833,0.006635165,0.04532072,0.0001994326,0.1374533,0.0006517852],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968565,0.001044916,0.001121754,0.0002307557,0.0002021789,0.0002742263,0.00006349335,0.00001124097,0.0001949645],"genre_scores_gemma":[0.9955288,0.00007198894,0.00200845,0.001437402,0.0001319738,0.00001598428,0.0003908693,0.00002406351,0.0003905042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3297756,"threshold_uncertainty_score":0.921244,"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."}}