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Amanda Lenzi et al.
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S. Roberts et al.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2013)
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Tilmann Gneiting et al.
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LJ Cao et al.
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