{"id":"W4398265573","doi":"10.7910/dvn/4s56hc/wpgtz6","title":"Massender_ssGBLUP_Type_Traits_ST2.xlsx","year":2022,"lang":"en","type":"supplementary-materials","venue":"Harvard Dataverse","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Breed; Cross-validation; Best linear unbiased prediction; Statistics; Biology; Computer science; Genetics; Artificial intelligence; Mathematics; Selection (genetic algorithm)","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":["insufficient_payload"],"category_scores_codex":[0.002356725,0.004999349,0.004258004,0.006872108,0.002385028,0.008297717,0.007136938,0.004848268,0.7949975],"category_scores_gemma":[0.009788727,0.003503156,0.0018122,0.01100929,0.001467355,0.006108524,0.006783481,0.003880556,0.6472538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002004513,"about_ca_system_score_gemma":0.002947914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009072159,"about_ca_topic_score_gemma":0.008973526,"domain_scores_codex":[0.9983895,0.0001973979,0.0001398589,0.0004163724,0.0004849366,0.0003718807],"domain_scores_gemma":[0.9958062,0.002032164,0.0003165489,0.0007438286,0.0005961839,0.0005049732],"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":[0.0002408233,0.00006393902,0.0006630468,0.002867849,0.00007387576,0.0001077754,0.0002413473,0.0002463281,0.0007865399,0.003020247,0.9851188,0.006569487],"study_design_scores_gemma":[0.0003908914,0.00002188252,0.002215606,0.0006285573,0.0000423641,0.00009450361,0.000155297,0.0002407887,0.001554755,0.005463817,0.9890879,0.0001035197],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001255987,0.0001030256,0.0007511669,0.0001364672,0.0000888909,0.00002801999,0.9837207,0.008799154,0.006246957],"genre_scores_gemma":[0.002310604,0.000371806,0.003114139,0.0003142171,0.00006648889,0.0003694002,0.9584653,0.02725859,0.007729477],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2050025,"threshold_uncertainty_score":0.2924112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362923456771999,"score_gpt":0.2357900847820294,"score_spread":0.2221608502143094,"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."}}