Quantifying ancient architecture: Stone-level machine learning reveals architectural variation and settlement growth at Shivta
- DOI
- 10.1073/pnas.2628851123
- Published
- 2026-10-28
- Container
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1073/pnas.2628851123,
title = {Quantifying ancient architecture: Stone-level machine learning reveals architectural variation and settlement growth at Shivta},
author = {Erel Uziel and Gideon Avni and Gary M. Feinman and Guy Bar-Oz and Yitzchak Y. Jaffe},
year = {2026},
journal = {Proceedings of the National Academy of Sciences},
doi = {10.1073/pnas.2628851123},
url = {https://doi.org/10.1073/pnas.2628851123}
}RIS
TY - JOUR TI - Quantifying ancient architecture: Stone-level machine learning reveals architectural variation and settlement growth at Shivta AU - Erel Uziel AU - Gideon Avni AU - Gary M. Feinman AU - Guy Bar-Oz AU - Yitzchak Y. Jaffe PY - 2026 JO - Proceedings of the National Academy of Sciences DO - 10.1073/pnas.2628851123 UR - https://doi.org/10.1073/pnas.2628851123 ER -
APA
Uziel, E., Avni, G., Feinman, G. M., Bar-Oz, G., & Jaffe, Y. Y. (2026). Quantifying ancient architecture: Stone-level machine learning reveals architectural variation and settlement growth at Shivta. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2628851123
Source records
- crossref · retrieved 2026-11-10T11:03:54.897Z