APA (7th ed.) Citation

Li, H., Zhang, Z., Zhang, R., Reichow, M. K., Cheng, Z., Zhang, Z., & Santosh, M. (2025). A machine-learning-based approach using clinopyroxene data to improve accuracy and efficiency in predicting tectonic settings: Implications for Rodinia supercontinent breakup triggered by mantle plume events. The American mineralogist, 110(7), 996-1011. https://doi.org/10.2138/am-2024-9535

Chicago Style (17th ed.) Citation

Li, Hengxu, Zhaochong Zhang, Ruixuan Zhang, Marc K. Reichow, Zhiguo Cheng, Zhenjie Zhang, and M. Santosh. "A Machine-learning-based Approach Using Clinopyroxene Data to Improve Accuracy and Efficiency in Predicting Tectonic Settings: Implications for Rodinia Supercontinent Breakup Triggered by Mantle Plume Events." The American Mineralogist 110, no. 7 (2025): 996-1011. https://doi.org/10.2138/am-2024-9535.

MLA (9th ed.) Citation

Li, Hengxu, et al. "A Machine-learning-based Approach Using Clinopyroxene Data to Improve Accuracy and Efficiency in Predicting Tectonic Settings: Implications for Rodinia Supercontinent Breakup Triggered by Mantle Plume Events." The American Mineralogist, vol. 110, no. 7, 2025, pp. 996-1011, https://doi.org/10.2138/am-2024-9535.