{"id":"W2977940813","doi":"10.1016/j.compag.2019.105032","title":"Predicting first test day milk yield of dairy heifers","year":2019,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Valacta (Canada)","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Concordance correlation coefficient; Dairy cattle; Pearson product-moment correlation coefficient; Statistics; Linear regression; Correlation coefficient; Animal science; Mathematics; Lactation; Herd; Regression analysis; Multivariate statistics; Correlation; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001470457,0.0002363194,0.0001926088,0.0002533403,0.0001239483,0.0002485517,0.0001288135,0.0002734356,0.0005383432],"category_scores_gemma":[0.0004845342,0.0000838875,0.0001278185,0.0001344514,0.00008245611,0.00009874511,0.00009282628,0.000166775,0.0001912089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002223301,"about_ca_system_score_gemma":0.0001221385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007846951,"about_ca_topic_score_gemma":0.0108826,"domain_scores_codex":[0.9999497,0.00001201803,0.000002071141,0.00001391862,0.000008676281,0.00001363266],"domain_scores_gemma":[0.9997479,0.0001385765,0.00002836966,0.000007164112,0.00003562763,0.00004226901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005286036,0.0001169332,0.9634128,0.00001088057,0.0000480591,0.0001220121,0.00005198674,0.004076361,0.02197904,0.00003217054,0.0001101304,0.009510914],"study_design_scores_gemma":[0.000005612555,0.0002737784,0.9780927,0.000001711563,0.00002086123,0.0001272776,0.00009328156,0.01650184,0.004759679,0.00004114847,0.00007774672,0.000004409632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995123,0.00001820827,0.0003027935,0.000003735237,7.046631e-7,8.563952e-7,0.00006593092,0.000005582141,0.00008981769],"genre_scores_gemma":[0.9993414,0.00001536634,0.0001588484,0.000003360492,7.666183e-7,9.114851e-7,0.0001555217,0.00000153285,0.0003222196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007846951,"threshold_uncertainty_score":0.01560253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004109963175408755,"score_gpt":0.1847856439706214,"score_spread":0.1806756807952127,"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."}}