Method / resourcemodel-written summary

A gradient boosting model was appropriate for predicting from the measured data.

A claim from Optimisation of Anaerobic Digestate and Chemical Fertiliser Application to Enhance Rice Yield—A Machine-Learning Approach · 2023

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

A gradient boosting model was appropriate for predicting from the measured data.

Outcome
appropriate for predicting the modellinge0

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValueQualifier

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

Oryza sativa
Oryza sativa · Taxon · picture from its Wikimedia Commons categoryDesmanthus4food · CC BY-SA 3.0 us · Wikimedia Commons ↗

Verbatim

e0

“The gradient‐boosting machine‐learning model was appropriate for predicting the modelling based on the measured data.”

✓ located after whitespace/quote normalisation · abstract, characters 932–1049
t0

“Rice”

✓ located verbatim in the source · title, characters 83–87

Title

Optimisation of Anaerobic Digestate and Chemical Fertiliser Application to Enhance Rice Yield—A Machine-Learning Approach

Binoy Kumar Show; Suraj Panja; Richik GhoshThakur; et al. Sustainability 2023. doi:10.3390/su151813706

Other claims from this paper (4)

Record rev-22751d7e39402f73f6919766951d0917fcba75eff506cf26422cf95ce2fb5b1f · schema claims-v0.9 · position 3 in the model’s answer