{"id":"W4394446924","doi":"10.6084/m9.figshare.21282409","title":"Additional file 1 of Design and validation of a 63K genome-wide SNP-genotyping platform for caribou/reindeer (Rangifer tarandus)","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier universitaire de Québec; Ministère des Ressources naturelles et des Forêts; University of Calgary; Université Laval","funders":"","keywords":"Genotyping; SNP; Biology; Genome; SNP genotyping; Computational biology; Genetics; Single-nucleotide polymorphism; Genotype; Gene","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002550256,0.001910976,0.002068307,0.003125205,0.001320273,0.002249869,0.003582998,0.002203981,0.4900858],"category_scores_gemma":[0.01051624,0.001177287,0.001571102,0.004064586,0.0006163386,0.001447568,0.001675036,0.001569134,0.1712031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001303149,"about_ca_system_score_gemma":0.003113673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01341806,"about_ca_topic_score_gemma":0.03019026,"domain_scores_codex":[0.9987703,0.0002106517,0.0001385333,0.0004794569,0.0002193274,0.0001816473],"domain_scores_gemma":[0.9931463,0.004135166,0.0004126389,0.001000962,0.0009156946,0.0003893788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003088576,0.0001043736,0.003230567,0.002802924,0.0001618017,0.00006207424,0.00008787898,0.00116969,0.0008874574,0.0009847402,0.9859492,0.004250407],"study_design_scores_gemma":[0.003870756,0.0001570363,0.02225648,0.001341218,0.000448937,0.0002676883,0.0002124446,0.001868506,0.002723437,0.008106431,0.9585662,0.0001807867],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007595681,0.000008825866,0.0001537306,0.00001394016,0.000005587758,0.00001848015,0.9993653,0.0001809112,0.0001773282],"genre_scores_gemma":[0.0007642797,0.00002023939,0.001175902,0.00008561257,0.000005745791,0.0003168111,0.9965066,0.0003339599,0.0007908485],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4900858,"threshold_uncertainty_score":0.7273307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03513738904532236,"score_gpt":0.2645600108309915,"score_spread":0.2294226217856692,"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."}}