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    WetCH<sub>4</sub>: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 by Q. Ying, Q. Ying, B. Poulter, J. D. Watts, K. A. Arndt, A.-M. Virkkala, L. Bruhwiler, Y. Oh, Y. Oh, B. M. Rogers, S. M. Natali, H. Sullivan, A. Armstrong, A. Armstrong, E. J. Ward, E. J. Ward, L. D. Schiferl, C. D. Elder, C. D. Elder, O. Peltola, A. Bartsch, A. R. Desai, E. Euskirchen, M. Göckede, B. Lehner, M. B. Nilsson, M. Peichl, O. Sonnentag, E.-S. Tuittila, T. Sachs, T. Sachs, A. Kalhori, M. Ueyama, Z. Zhang, Z. Zhang

    Published 2025-06-01
    “…The most important predictor<span id="page2508"/> variables included near-surface soil temperatures (top 40 cm), vegetation spectral reflectance, and soil moisture. Our results, modeled from 138 site years across 26 sites, had relatively strong predictive skill, with a mean <span class="inline-formula"><i>R</i><sup>2</sup></span> of 0.51 and 0.70 and a mean absolute error (MAE) of 30 and 27 nmol m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span> for daily and monthly fluxes, respectively. …”
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    Evaluating treatment strategies and machine learning based treatment recommendation system for elderly patients with high grade gliomas by Feiling Xiang, Feiling Xiang, Mengyuan Fu, Mengyuan Fu, Xuelian Yang, Xuelian Yang

    Published 2025-08-01
    “…This study aims to evaluate the benefit of treatment strategies and develop a treatment recommendation system for eHGG patients.MethodsBy propensity score matching and survival analysis, we compared the prognosis of treatment strategies, including surgery versus none, adjuvant therapies versus none, and gross total resection (GTR) versus subtotal resection (STR), among patients aged 65 and older with high-grade gliomas. A machine learning model, random survival forest, was developed to provide predictions on prognosis. …”
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