New machine learning models on reevaluation of the Ti-in-zircon thermometer <i>via</i> multivariate trace elements
Through training and testing extensive machine learning algorithms, an advanced high-dimensional temperature prediction model based on the XGBoost has been developed, which utilizes comprehensive trace element concentrations within zircons.
Authors
- Chen Hongjie
- Ying‐Ming Sheng (ORCID: https://orcid.org/0009-0006-9446-478X)
Institutions
- University of Science and Technology of China (CN)
Publication Details
- Journal
- Journal of Analytical Atomic Spectrometry
- Published
- 2024-11-20
- DOI
- https://doi.org/10.1039/d4ja00352g
- Primary Topic
- Iron and Steelmaking Processes
- Type
- article
- Field-Weighted Citation Impact
- 0.00