Degrees of uncertainty: conformal deep learning for non-invasive core body temperature prediction in extreme environments
- DOI
- 10.1038/s44172-025-00548-6
- Published
- 2025-11-20
- Container
- Communications Engineering
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s44172-025-00548-6,
title = {Degrees of uncertainty: conformal deep learning for non-invasive core body temperature prediction in extreme environments},
author = {Joel Strickland and Marco Ghisoni and Hannah Marshall and Thomas Whitehead and Bogdan Nenchev and Ben Pellegrini and Charles Phillips and Karl Tassenberg and Sarah Davey and Sandra Dorman and Joseph Sol and David Ferguson and Gareth Conduit},
year = {2025},
journal = {Communications Engineering},
doi = {10.1038/s44172-025-00548-6},
url = {https://doi.org/10.1038/s44172-025-00548-6}
}RIS
TY - JOUR TI - Degrees of uncertainty: conformal deep learning for non-invasive core body temperature prediction in extreme environments AU - Joel Strickland AU - Marco Ghisoni AU - Hannah Marshall AU - Thomas Whitehead AU - Bogdan Nenchev AU - Ben Pellegrini AU - Charles Phillips AU - Karl Tassenberg AU - Sarah Davey AU - Sandra Dorman AU - Joseph Sol AU - David Ferguson AU - Gareth Conduit PY - 2025 JO - Communications Engineering DO - 10.1038/s44172-025-00548-6 UR - https://doi.org/10.1038/s44172-025-00548-6 ER -
APA
Strickland, J., Ghisoni, M., Marshall, H., Whitehead, T., Nenchev, B., Pellegrini, B., Phillips, C., Tassenberg, K., Davey, S., Dorman, S., Sol, J., Ferguson, D., & Conduit, G. (2025). Degrees of uncertainty: conformal deep learning for non-invasive core body temperature prediction in extreme environments. Communications Engineering. https://doi.org/10.1038/s44172-025-00548-6
Source records
- crossref · retrieved 2026-09-25T13:58:50.569Z