Using Monte Carlo conformal prediction to evaluate the uncertainty of deep-learning soil spectral models
<p>Uncertainty quantification is a crucial step in the practical application of soil spectral models, particularly in supporting real-world decision making and risk assessment. While machine learning has made remarkable strides in predicting various physiochemical properties of soils using spe...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
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| Series: | SOIL |
| Online Access: | https://soil.copernicus.org/articles/11/553/2025/soil-11-553-2025.pdf |
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