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Quantitative fits are good and the model parameters have plausible values.
For rice, it is found that the substrate-competing model is sufficient for quantitative fits (Wu et al. 2013).
However, as explained later, this model does not provide quantitative fits to the observed dependence of polarization on phase angle.
Though better quantitative fits would likely be obtained with more sophisticated models including further parameters [e.g. 2], [16], the following qualitative features are common across models.
Incorporating this assumption into the leaky competing accumulator model, we are able to provide close quantitative fits to individual participant data.
Less recent studies did not use quantitative FITs or did not evaluate test characteristics at different cut-off values [ 17, 20].
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Quantitative fit testing (QNFT) is the gold standard used to determine this fit objectively.
However, it experiences some difficulties in providing a quantitative fit to the observational data.
The models predict an explicit BDT, and give a good quantitative fit to the experimental transition temperatures.
This visual assessment is confirmed by the quantitative fit of the line of identity (y = x) to the datasets.
However, a quantitative fit to the observed degree of polarization seemed to require very big, made of hundreds of thousand monomers, aggregates.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com