main webpage W Topic The Time Machine UNC Libraries Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale 2025 Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements… Article Open The Time Machine Open Article Page

The Time Machine

1895 dystopian post-apocalyptic science fiction novella by H. G. Wells The Time Machine is an 1895 dystopian post-apocalyptic science fiction novella by H. G. Wells about a Victorian scientist known as the Time Traveller who travels approximately 800,806 years into the future. The work is generally credited with the popularization of the concept of time travel by using a vehicle or device to travel purposely and selectively forward or backward through time. The term "time machine", coined by Wells, is now almost universally used to refer to such a vehicle or device. Utilizing a frame story set in then-present Victorian England, Wells' text focuses on a recount of the otherwise anonymous Time Traveller's journey into the far future. Critical Symbolic Virtual Narrative Open The Time Machine Open Article Page

Exploring foci of: UNC Libraries Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale 2025 Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space&ndash;time resolution. This study inv… Open Article Page

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Explore The Time Machine W Topic The Time Machine UNC Libraries Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale 2025 Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements… Article Critical Symbolic Virtual Narrative Open The Time Machine Open Article Page