Based on such knowledge, empirical recommendations may be customized.
However, the question of the robustness of empirical studies based on such indicators has become increasingly critical.
Estimates based on such methods have been widely used [33], [34], [46], [47] but they can conflict with empirical approaches that are based on laboratory measurements of generation lengths and mutation rates [48], [49].
By Bayes factor evaluation, we have shown previously that CAT generally has a better fit than homogeneous models based on one single empirical substitution matrix, such as WAG [ 39], JTT [ 40], or even the most general site-homogeneous and time-reversible model (GTR) [ 31, 41].
This strand is based on empirical literature such as Magnac (1991); Maloney (2004); Pratap and Quintin (2006), and Bucheli and Ceni (2010).
Summative (demand-side) evaluation of interventions is based on direct empirical evidence, such as that derived from head-to-head comparisons.
We therefore utilized the method of modified scaled distances (SDs) based on empirical equations considering such factors as blasting parameters and geological parameters of rock mass for bench blasting in quarries.
Finally, methods for adjusting model parameters based on empirical uncertainty estimates, such as regression calibration, are questionable because each source category is an aggregate of numerous pollutants each with their own error distribution and covariances.
This paper develops such a hierarchy based on current empirical and theoretical evidence.
In any application, such assignments should be based on sound empirical evidence.
