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.

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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
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article

New machine learning models on reevaluation of the Ti-in-zircon thermometer via multivariate trace elements

Chen Hongjie, Ying‐Ming Sheng
Journal of Analytical Atomic Spectrometry
Iron and Steelmaking Processes
article

New machine learning models on reevaluation of the Ti-in-zircon thermometer via multivariate trace elements

Chen Hongjie, Ying‐Ming Sheng
article en

Abstract

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.

Journal of Analytical Atomic SpectrometryVol. 40(2)
University of Science and Technology of China (CN)
Openalex Percentile: Top 79%
Iron and Steelmaking Processes
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