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The agency's model does not predict losses in any given year but provides a comparison of damage to various cities over centuries.
However, the pilot project still faces the challenge of developing a viable and flexible index that could predict losses.
Heatwole and Rose (2013) proposed a specific reduced-form model based on U.S. earthquakes that considered a linear regression model of predictor variables to predict losses.
The findings above combined with the findings of (Turpie et al. 2010) were used to predict losses in livestock and wildlife numbers in Namibia, resulting from climate change.
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To predict loss of OCT1 function we used eight independent prediction tools.
Both models were developed by modular neural network, to predict loss circulation in quality and quantity.
Michael Madowitz from the Center for American Progress developed a wage-loss calculator to predict loss of income as a result of time off due to childcare.
However, area deprivation did not predict loss to follow-up.
Furthermore, precise tools are required to predict loss of response, or disease progression, in patients treated with lenalidomide.
Thus, CNS loss of detectability could predict loss of a specific regulatory function, but only in the case that the CNS loss marks loss of CNS function.
Instead, they predicted losses climbing to $40 million to $52 million in 2002.
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