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Liu et al. [11] proposed two models called Random Linear monotonic map Geodesic flow kernel model (RLG) and Grassmann Linear monotonic map Geodesic flow kernel model (GLG).
The developed agglomeration kernel model incorporates terms for the collision, capture, rupture and cementation rates affecting the formation of agglomerates.
The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm.
The kernel model may be a useful tool to quantify the LST uncertainties associated with viewing and illumination angles.
In this study, I examine a recent resistant kernel model designed to prioritize amphibian breeding habitat for conservation.
The key idea is to integrate the knowledge encoded in a kernel model into a Gaussian mixture scheme.
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There is still room for improvement by better kernel modeling and offset corrections.
New correctors for coalescence rate could then be derived for various kernel models.
Dispersal kernel modeling using the neighborhood model indicated that pollen dispersal of A. mono was highly fat-tailed.
It is designed around a kernel modelling, a generic patchy landscape that could be progressively driven to follow natural (climatic, ecological…) forces and human (sociological, economical…) decisions.
The method is based on calculating uncertainty spheres for a specific confidence level obtained by kernel modelling through a weighted resampling of the point cloud.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com