An Interpretable Machine Learning Approach to Ecologically Characterize Soil Carbon and Structure From Multi-Kingdom Microbiome, Texture and Climate.
- DOI
- 10.1111/mec.70535
- Published
- 2026 Sep
- Container
- Molecular ecology
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1111/mec.70535,
title = {An Interpretable Machine Learning Approach to Ecologically Characterize Soil Carbon and Structure From Multi-Kingdom Microbiome, Texture and Climate.},
author = {Jeanne T and Prunier J and Hogue R and Droit A},
year = {2026},
journal = {Molecular ecology},
doi = {10.1111/mec.70535},
url = {https://doi.org/10.1111/mec.70535}
}RIS
TY - JOUR TI - An Interpretable Machine Learning Approach to Ecologically Characterize Soil Carbon and Structure From Multi-Kingdom Microbiome, Texture and Climate. AU - Jeanne T AU - Prunier J AU - Hogue R AU - Droit A PY - 2026 JO - Molecular ecology DO - 10.1111/mec.70535 UR - https://doi.org/10.1111/mec.70535 ER -
APA
T, J., J, P., R, H., & A, D. (2026). An Interpretable Machine Learning Approach to Ecologically Characterize Soil Carbon and Structure From Multi-Kingdom Microbiome, Texture and Climate.. Molecular ecology. https://doi.org/10.1111/mec.70535
Source records
- pubmed · retrieved 2026-09-25T12:50:11.413Z