{"id":"W7100154844","doi":"","title":"RESEARCH ARTICLE Open Access Prediction accuracies for growth and wood","year":2016,"lang":"en","type":"article","venue":"","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Best linear unbiased prediction; Predictability; Genetic gain; Imputation (statistics); Selection (genetic algorithm); Heritability; Trait; Predictive modelling; Tree breeding; Regression","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":[0.003928968,0.0006984776,0.000561669,0.001399754,0.0003360097,0.001190044,0.0008763937,0.0008115352,0.01386987],"category_scores_gemma":[0.01376263,0.0001753011,0.0008913483,0.001118314,0.0002425689,0.0009737706,0.0007027636,0.0005891619,0.007089296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005564606,"about_ca_system_score_gemma":0.0007965914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01370217,"about_ca_topic_score_gemma":0.01188898,"domain_scores_codex":[0.9982675,0.0004400219,0.00008591236,0.0006828198,0.0003783848,0.0001452119],"domain_scores_gemma":[0.9924338,0.00379955,0.0007692449,0.001137733,0.001671418,0.0001883218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007257095,0.0002391113,0.2961525,0.0001878577,0.0004003902,0.0001950296,0.0001044939,0.1451973,0.004883714,0.001865129,0.01220456,0.5378442],"study_design_scores_gemma":[0.00006028622,0.0002215507,0.1282364,0.00008967215,0.0001311894,0.0004046551,0.0001374726,0.850899,0.006979599,0.004616213,0.008162784,0.00006120745],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6482233,0.001269428,0.3080275,0.0006144384,0.0002930471,0.0001280031,0.01588264,0.01021228,0.0153493],"genre_scores_gemma":[0.9128051,0.0002921878,0.06551503,0.00007086507,0.00008152211,0.00006212643,0.01164338,0.0004483536,0.009081328],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9861301,"threshold_uncertainty_score":0.04639935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08549521526910935,"score_gpt":0.3978076267948032,"score_spread":0.3123124115256939,"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."}}