{"id":"W6964743334","doi":"10.3389/fgene.2021.665344.s003","title":"Image_3_Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations.JPEG","year":2021,"lang":"en","type":"other","venue":"Figshare","topic":"Biomedical and Chemical Research","field":"Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0005920217,0.00063126,0.0003673394,0.00125435,0.0004579552,0.0009754047,0.0008499091,0.0005231794,0.027595],"category_scores_gemma":[0.001287979,0.0003624836,0.0005916742,0.001059087,0.0002196068,0.0002982517,0.0004272044,0.0003879979,0.006075518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00118209,"about_ca_system_score_gemma":0.001584775,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3682887,"about_ca_topic_score_gemma":0.5130515,"domain_scores_codex":[0.9997975,0.00001335512,0.000005106871,0.00005258267,0.00009210142,0.0000392899],"domain_scores_gemma":[0.9995894,0.00009142171,0.00003186569,0.00003844524,0.000222679,0.00002620283],"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":[0.001644009,0.0001181918,0.1409895,0.000995726,0.0004101573,0.0004491143,0.0005799884,0.01120897,0.08725298,0.002600307,0.4043817,0.3493693],"study_design_scores_gemma":[0.0002879744,0.0001571223,0.5726299,0.0003107981,0.0003162101,0.0006633744,0.0003692681,0.1130887,0.04536463,0.001824155,0.2647599,0.000227908],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.2335306,0.002300958,0.1118419,0.00154911,0.0005901728,0.0004428294,0.5424982,0.04856041,0.05868577],"genre_scores_gemma":[0.4271473,0.001091097,0.1420633,0.0007999442,0.00009295267,0.0004579215,0.3878472,0.005148141,0.03535206],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6317113,"threshold_uncertainty_score":0.73229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1039361324551697,"score_gpt":0.3140550343267852,"score_spread":0.2101189018716154,"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."}}