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For each step, the QSPR model was parameterized on the training set.
The correlation model was parameterized using the normal boiling point and specific gravity at 60 °F.
The conductivity model was parameterized using 5 layers each 200 km thick with spherical harmonic expansion truncated at.
This method provides charges comparable to the QM approach for which the given EEM model was parameterized.
The model was parameterized with λ=3.5 and μ=1.8, and 10 networks with 107 nodes were generated.
The model was parameterized for green cell growth of Haematococcus pluvialis using the Gauss-Newton and bootstrap methods.
The computational dual-control model was parameterized to assess separate weights for habitual versus goal-directed learning.
The model was parameterized with ecological, forest inventory and historical land-use data, and run using historical meteorological observations.
The model was parameterized with values that are representative of the Everglades wetland in the southern portion of the Florida peninsula in the USA.
The mathematical model was parameterized from experiments with two-phase flow through the packed bed and then applied to the singular phase flow mode.
The model was parameterized to have staging characteristics similar to data published by the Surveillance, Epidemiology, and End-Results (SEER) Program.
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