2017 ICARDA Barley Net Form and Spot Blotch FIGS Subset
<|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:'The FIGSICBNetFormSpotBlotch2017 set was constructed using evaluation data from more than 875 Barley accessions (http://dx.doi.org/10.1094/PHYTO-04-14-0107-R) and machine learning algorithms including Random Forest, support vector machine and kernel nearest neighbors, and using daily climatic data aligned to onset to capture durum wheat physiological stage. The metrics from the machine learning were medium to high (Accuracy = 0.70 and Kappa = 0.25). 200 accessions were then selected based on higher predictive probability. FIGS_ICB_NetFormSpotBlotch_2017 is the name of the FIGS subset including the crop, trait name and the year of construction' . 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" : ["barley","spotblotch","zero hunger"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The FIGSICBNetFormSpotBlotch2017 set was constructed using evaluation data from more than 875 Barley accessions (http://dx.doi.org/10.1094/PHYTO-04-14-0107-R) and machine learning algorithms including Random Forest, support vector machine and kernel nearest neighbors, and using daily climatic data aligned to onset to capture durum wheat physiological stage. The metrics from the machine learning were medium to high (Accuracy = 0.70 and Kappa = 0.25). 200 accessions were then selected based on higher predictive probability. FIGS_ICB_NetFormSpotBlotch_2017 is the name of the FIGS subset including the crop, trait name and the year of construction' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The FIGSICBNetFormSpotBlotch2017 set was constructed using evaluation data from more than 875 Barley accessions (http://dx.doi.org/10.1094/PHYTO-04-14-0107-R) and machine learning algorithms including Random Forest, support vector machine and kernel nearest neighbors, and using daily climatic data aligned to onset to capture durum wheat physiological stage. The metrics from the machine learning were medium to high (Accuracy = 0.70 and Kappa = 0.25). 200 accessions were then selected based on higher predictive probability. FIGS_ICB_NetFormSpotBlotch_2017 is the name of the FIGS subset including the crop, trait name and the year of construction
barley; spotblotch; zero hunger