Machine learning as an interpretive consistency approach for coupling geochemical and geophysical domains in data-limited landfills
- DOI
- 10.1093/pnasnexus/pgag235
- Published
- 2026-06-29
- Container
- PNAS Nexus
- Publisher
- Oxford University Press (OUP)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1093/pnasnexus/pgag235,
title = {Machine learning as an interpretive consistency approach for coupling geochemical and geophysical domains in data-limited landfills},
author = {Vincenzo Costanzo-Álvarez and Maria Jácome and Milagrosa Aldana and Rosario Trigo-Ferre and Melanie Jeffrey and Vincenzo Luciano Costanzo and Amaru Izarra-Jácome and Cristina H Amon},
year = {2026},
journal = {PNAS Nexus},
doi = {10.1093/pnasnexus/pgag235},
url = {https://doi.org/10.1093/pnasnexus/pgag235}
}RIS
TY - JOUR TI - Machine learning as an interpretive consistency approach for coupling geochemical and geophysical domains in data-limited landfills AU - Vincenzo Costanzo-Álvarez AU - Maria Jácome AU - Milagrosa Aldana AU - Rosario Trigo-Ferre AU - Melanie Jeffrey AU - Vincenzo Luciano Costanzo AU - Amaru Izarra-Jácome AU - Cristina H Amon PY - 2026 JO - PNAS Nexus DO - 10.1093/pnasnexus/pgag235 UR - https://doi.org/10.1093/pnasnexus/pgag235 ER -
APA
Costanzo-Álvarez, V., Jácome, M., Aldana, M., Trigo-Ferre, R., Jeffrey, M., Costanzo, V. L., Izarra-Jácome, A., & Amon, C. H. (2026). Machine learning as an interpretive consistency approach for coupling geochemical and geophysical domains in data-limited landfills. PNAS Nexus. https://doi.org/10.1093/pnasnexus/pgag235
Source records
- crossref · retrieved 2026-09-25T16:48:13.152Z