Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation
Abstract Global warming is one of the most pressing and critical problems facing the world today. It is mainly caused by the increase in greenhouse gases in the atmosphere, such as carbon dioxide (CO2). Understanding how soils respond to rising temperatures is critical for predicting carbon release...
Saved in:
| Main Authors: | Pierfrancesco Novielli, Michele Magarelli, Donato Romano, Pierpaolo Di Bitonto, Anna Maria Stellacci, Alfonso Monaco, Nicola Amoroso, Roberto Bellotti, Sabina Tangaro |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-96216-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data
by: Donato Romano, et al.
Published: (2025-04-01) -
Personalized colorectal cancer risk assessment through explainable AI and Gut microbiome profiling
by: Pierfrancesco Novielli, et al.
Published: (2025-12-01) -
Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
by: Rameez Ahsen, et al.
Published: (2025-04-01) -
Pre Hoc and Co Hoc Explainability: Frameworks for Integrating Interpretability into Machine Learning Training for Enhanced Transparency and Performance
by: Cagla Acun, et al.
Published: (2025-07-01) -
An Efficient Explainability of Deep Models on Medical Images
by: Salim Khiat, et al.
Published: (2025-04-01)