OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Estimation of daily apple tree transpiration in the Loess Plateau region of China using deep learning models
Liwen Xing, Ningbo Cui, Chunwei Liu, et al.
Agricultural Water Management (2022) Vol. 273, pp. 107889-107889
Closed Access | Times Cited: 8

Showing 8 citing articles:

Estimating reference evapotranspiration using Penman-Monteith equation integrated with optimized solar radiation models
Liwen Xing, Yu Feng, Ningbo Cui, et al.
Journal of Hydrology (2023) Vol. 620, pp. 129407-129407
Closed Access | Times Cited: 18

Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning
Rajib Maity, Aman Srivastava, Subharthi Sarkar, et al.
Applied Computing and Geosciences (2024) Vol. 24, pp. 100206-100206
Open Access | Times Cited: 5

Estimating daily kiwifruit evapotranspiration under regulated deficit irrigation strategy using optimized surface resistance based model
Liwen Xing, Ningbo Cui, Chunwei Liu, et al.
Agricultural Water Management (2024) Vol. 295, pp. 108745-108745
Open Access | Times Cited: 4

Estimating the transpiration of kiwifruit using an optimized canopy resistance model based on the synthesis of sunlit and shaded leaves
Zongyang Li, Lu Zhao, Zhongkuo Zhao, et al.
Agricultural Water Management (2024) Vol. 306, pp. 109193-109193
Open Access

Comparative Assessment of Sap Flow Modeling Techniques in European Beech Trees: Can Linear Models Compete with Random Forest, Extreme Gradient Boosting, and Neural Networks?
Paulína Nalevanková, Peter Fleischer, Mohammad Mukarram, et al.
Water (2023) Vol. 15, Iss. 14, pp. 2525-2525
Open Access | Times Cited: 1

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