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Source the Gini coefficient includes only Buenos Aires and its metropolitan area; it was computed using the Socioeconomic Database of Latin America and the Caribbean (CEDLAS-WB); the Real GDPpc are values reported in World Development Indicators WBB).
Before it was computed using the psftab calibration at 1.0keV for a 0.393 ECF ellipse.
It was computed using the Weibull's plotting position formula and applied to the observed rainfall data.
It was computed using the SCIP 2.1.1 solver, which is able to deal with non-linearity by applying an interior point optimizer (GAMS 2012).
It was computed using the equation: W = {raise0.7exhbox{${left( {W_{text{r}} ; times ;W_{text{w}} } right)}$} mathord{left// {vphantom {{left( {W_{text{r}} ; times ;W_{text{w}} } right)} {V_{text{f}} ; times ;100}}}right.kern-0pt} !lower0.7exhbox{${V_{text{f}} ; times ;100}$}}where W is the effective weight, W r and W w are the rating value and weight of each parameter, V f is the final model.
It was computed using the software program Programita kindly provided by Thorsten Wiegand.
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It is computed using a forward-backward algorithm over the lattice.
It is computed using network evidence collected from the observer nodes in the network.
It is computed using the number of the candidates' neighbors in the multiplex network.
It is computed using the brightness constraint, which assumes brightness constancy of corresponding pixels in consecutive frames.
It is a "static" measure of hazard, given the fact that it is computed using the current state of a site that has not necessarily yet slid.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com