Method / resourcemodel-written summary

The compact XGB model had bias of − 0.06 MPa.

A claim from A machine learning framework for California vineyard water status monitoring using sUAS Imagery and short-term meteorological data · 2026

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The compact XGB model had bias of − 0.06 MPa.

Taxon
grapevineVitist0
Outcome
biase0
Value
− 0.06 MPae0

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Vitis
Vitis · Taxon · picture of the genus (one of its species)Victor M. Vicente Selvas · Public domain · Wikimedia Commons ↗

Verbatim

e0

“achieved R 2 = 0.63, RMSE = 0.16 MPa , and bias = − 0.06 MPa”

✓ located verbatim in the source · abstract, characters 1030–1090
t0

“grapevine leaf water potential”

✓ located verbatim in the source · abstract, characters 525–555

Title

A machine learning framework for California vineyard water status monitoring using sUAS Imagery and short-term meteorological data

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)

Record rev-11b3d1177232f045477d07e85b423ad123137266c75e15f25595d6b2e54e3b9f · schema claims-v0.9 · position 6 in the model’s answer