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Our results demonstrate that the analytical approach and modeling we used are useful for investigations of earthquake slip, not only for sediment-hosted thrusts at subduction boundaries (Ishikawa et al. 2008; Hamada et al. 2011; Honda et al. 2011), but also for active inland granite-hosted faults.
To demonstrate that dense network of distance constraints benefits the molecular modeling, we used all the generated XL distance constraint for local perturbation modeling to create a comprehensive binding model of the M1 protein on the bacterial surface (Fig. 4b).
For the processing of spatial data we applied GIS techniques using the ArcGIS software (ESRI) and for the modeling we used the InVEST 3.2 software (Natural Capital Project).
For the geospatial modeling, we used soil profile observations (585 for SOC stocks and 153 for active-layer thickness) and environmental variables (climate, topography, land cover, and surficial geology types) and generated fine-resolution (50-m spatial resolution) predictions of SOC stocks (to 1-m depth) and active-layer thickness across Alaska.
For modeling we used two experimental systems.
According to the generally accepted convention for earthquake source modeling, we used the same seismic moment for all subsources.
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Generally, in mathematical modeling we use Caputo's definition.
For modeling we use the "Enter" method on SPSS.
For the selected set of models we used model averaging to address model uncertainty64.
For other models, we used default parameters set in the scikit-learn library.
So what happened to the Turkish model we used to be so hopeful about?
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