Reliable machine learning initialization methods for the calibration of Dynamic Energy Budget models
- DOI
- 10.1016/j.ecoinf.2026.103624
- Published
- 2026-03
- Container
- Ecological Informatics
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.ecoinf.2026.103624,
title = {Reliable machine learning initialization methods for the calibration of Dynamic Energy Budget models},
author = {Diogo F. Oliveira and Gonçalo M. Marques and Filipe M.P. Santos and Laure Pecquerie and João M.C. Sousa and Tiago Domingos},
year = {2026},
journal = {Ecological Informatics},
doi = {10.1016/j.ecoinf.2026.103624},
url = {https://doi.org/10.1016/j.ecoinf.2026.103624}
}RIS
TY - JOUR TI - Reliable machine learning initialization methods for the calibration of Dynamic Energy Budget models AU - Diogo F. Oliveira AU - Gonçalo M. Marques AU - Filipe M.P. Santos AU - Laure Pecquerie AU - João M.C. Sousa AU - Tiago Domingos PY - 2026 JO - Ecological Informatics DO - 10.1016/j.ecoinf.2026.103624 UR - https://doi.org/10.1016/j.ecoinf.2026.103624 ER -
APA
Oliveira, D. F., Marques, G. M., Santos, F. M., Pecquerie, L., Sousa, J. M., & Domingos, T. (2026). Reliable machine learning initialization methods for the calibration of Dynamic Energy Budget models. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2026.103624
Source records
- crossref · retrieved 2026-09-26T10:39:43.616Z