{"id":"W6927415760","doi":"10.3389/fgene.2021.665344.s006","title":"Table_3_Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations.docx","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Ichthyology and Marine Biology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Best linear unbiased prediction; Loin; Imputation (statistics); Genomic selection; Large white; Genetic gain; Predictive modelling; Mean squared prediction error; Animal breeding","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008074216,0.001196939,0.0008400713,0.00258803,0.001414013,0.001407991,0.001989802,0.0005853976,0.3177604],"category_scores_gemma":[0.005540209,0.0006849856,0.001198584,0.003251913,0.0003522801,0.0006660174,0.00065242,0.0009272295,0.0390694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002211621,"about_ca_system_score_gemma":0.00317647,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3064514,"about_ca_topic_score_gemma":0.5091597,"domain_scores_codex":[0.9996525,0.00002442596,0.00002600795,0.0001012576,0.0001326871,0.00006318175],"domain_scores_gemma":[0.9970674,0.001208136,0.000189518,0.0001820143,0.001202925,0.0001500189],"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":[0.0005001988,0.00007186412,0.05437025,0.002705716,0.0002707356,0.0003492425,0.000428848,0.002376719,0.00536902,0.0008262972,0.8902076,0.04252355],"study_design_scores_gemma":[0.0009674883,0.0001691611,0.3716691,0.001255816,0.0004271204,0.000660352,0.0009684538,0.007716981,0.007035453,0.002415777,0.6064839,0.0002302671],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004968055,0.0001355606,0.002078291,0.0001522056,0.00008885104,0.0001274012,0.9881189,0.001372462,0.002958169],"genre_scores_gemma":[0.02482096,0.0002867684,0.01729785,0.0005581504,0.00005660218,0.0005693163,0.9374387,0.001312664,0.01765908],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6935486,"threshold_uncertainty_score":0.973132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04427074863867145,"score_gpt":0.2454326703992161,"score_spread":0.2011619217605446,"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."}}