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Assessing the robustness and generalizability of machine learning models for predicting selenium content in rice: a case study from the Pearl River Delta and Eastern Guangdong, China

Crossref DOI link: https://doi.org/10.1007/s10653-025-02681-9

Published Online: 2025-08-13

Published Print: 2025-09

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Ye, Guiqi

Li, Tingting

Geng, Wenda

Qian, Kun

Ma, Xudong

Hou, Qingye

Yu, Tao

Yang, Zhongfang

Zhu, Xin
Funding

Funding for this research was provided by:

Zhongfang Yang (2024-16, 2023-25, 2024-16, 2023-25, 2024-16, 2023-25, 2024-16, 2023-25, 2024-16, 2023-25, 2024-16, 2023-25, 2024-16, 2023-25, 2024-16, 2023-25, 2024-16, 2023-25)
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Text and Data Mining valid from 2025-08-13

Version of Record valid from 2025-08-13
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Article History

Received: 10 March 2025

Accepted: 21 July 2025

First Online: 13 August 2025

Declarations

:

: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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