Claim · model-written summary
The full XGB model had RMSE of 0.14 MPa.
VitisNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the trained eXtreme Gradient Boosting (XGB) model achieved R 2 = 0.71, RMSE = 0.14 MPa , and bias = − 0.06 MPa on the independent test dataset”
t0“grapevine leaf water potential”
Title
Rui Gao; Maria Mar Alsina; Alfonso F. Torres-Rua; et al. Irrigation Science 2026. doi:10.1007/s00271-026-01102-8
Other claims from this paper (6)
- Method / resource The full XGB model achieved R² of 0.71 for leaf water potential on the independent test data.
- Method / resource The full XGB model had bias of − 0.06 MPa.
- Method / resource The compact XGB model achieved R² of 0.63.
- Method / resource The compact XGB model had RMSE of 0.16 MPa.
- Method / resource The compact XGB model had bias of − 0.06 MPa.
- Method / resource Compact-model performance was not significantly different from full-model performance at α = 0.05.