A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Agricultural research","Bayesian multi-output regressor stacking","Breeding programs","GBLUP","Genomic best linear unbiased prediction","Genomic selection","Maize","Multi-environment","Multi-trait","Triticum aestivum","Wheat","Zea mays"]} <|eot_id|>
 Here is the Description of a dataset. Description:'A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data
Agricultural research; Bayesian multi-output regressor stacking; Breeding programs; GBLUP; Genomic best linear unbiased prediction; Genomic selection; Maize; Multi-environment; Multi-trait; Triticum aestivum; Wheat; Zea mays