John,
The data points to feed the model started out as measured parameters for the guitar that was modelled, but you have to remember that the values in the model are all equivalent masses and equivalent stiffnesses, which are hard to measure experimentally. However, they are mostly reasonably close to measurements made in fairly simple ways, unlike the values you get if you try to tune a 3 DOF model. The 3 DOF model is written up very well in Howard Wright's thesis, downloadable from
here and is pretty much essential reading if meddling in this stuff.
The stiffness K (N/m) [not sure how you got Nm, unless it was just a typo], is measured in the same way as it is measured for monopole mobility, just load/deflection with the load on the monopole antinode (for both top and back). Obviously, you need a finished guitar to do this with or some pretty good FE modelling. You could go back to the Hearmon equations, but they are only for rectangular flat plates, without bracing, so you would have to make some adjustments. Caldersmith, in one of his papers (the guitar as a reflex enclosure) has an approach that can get you to an equivalent braced plate stiffness from the plate dimensions and brace sizes. His numbers come out similar to my measurements. If you're trying to do predictive design work, probably your best bet is to find a guitar similar to what you want to design and go from there.
For the damping factors, they were matched to the frequency response curve of the modelled guitar and so they are what they are. As you know, the Q values of the peaks in the frequency response curve of a finished guitar bear little relationship to the Q of the materials it is made from.
Having fed the basic measurements into the model, the model is then "tuned" to match the frequency response curve of the guitar being modelled by tweaking the parameters until the difference between the modelled result and the measured guitar response is minimised, all the time making sure that as you tweak the parameters they still remain reasonably close to real world values. You could write a routine to do the tweaking (more optimisation!) or do it manually. I think most people (Wright, Christensen etc.) used a combination of both, as did I, as doing optimisation in this complex space presents its own problems.