I decided to do a USS-model run with the values in the DNA-uptake matrix increased five-fold, thinking this meant that drive favouring uptake sequences would be much stronger.  This should have meant that, when beginning with a random sequence, there would initially be very few fragments that had sufficiently high scores to pass the probability filter and recombine with the genome.  So I was surprised when the frequency of recombination at the beginning of the run looked to be about the same as with the normal matrix.

But then I remembered that one nice feature of the latest version of the model is that it uses whatever values are in the DNA-uptake matrix to calculate an appropriate value for the recombination probability filter, always equal to the score earned by a single site that perfectly matches the matrix-specified preferences.  This is good under some circumstances, but in the present case it entirely defeats what I was trying to do.

So I guess it's time to tweak the model one more time, introducing a coefficient by which the probability filter can be multiplied.  Ideally we'd like to be able to sometimes have this coefficient change during the run, as this would allow us to simulate evolution of the uptake specificity itself.  I'm confident that I can introduce a constant coefficient (read in with the other run settings); I wonder if I can easily make it a variable...