Thanks
Kay and Graeme for inputs.
I should, nevertheless, go deeper into this. I should say that,
in my experience, in some cases the effect is small, but in some
others, I would consider significative. I will try to be more
systematic when possible to devise situations, specially for the
latter.
Yours,
Jorge
On 02/21/2017 05:00 AM, Kay Diederichs
wrote:
I've also experienced this, but since the improvement is small, I did not pay much attention, and did not investigate. My hypothesis why this occurs agrees with yours. Nothing should prevent you to make use of this effect!
best,
Kay
On Mon, 20 Feb 2017 08:24:58 -0300, Jorge Iulek <jiu...@gmail.com> wrote:
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<font size="+1"><font face="Times New Roman, Times, serif">Dear all,<br>
<br>
I have been noticing, with many datasets, processed the duet
xds/scale, that when one integrates to a resolution limit which
is (a little) higher than the one used for scaling/merging, the
statistics (and here I mean R-symm, R-meas, <I/sigI> and
even CC1//2) get better (I might also advance that, in many
cases, completeness gets a little better too).<br>
Just to make clear, suppose I want to process to resolution
x (according to any criteria/index I decide) ; suppose y is a
little (how much is yet another good discussion) higher
resolution, id est, numerically, x > y, I get better
statistics when I integrate up to y and scale/merge to x, rather
than using x in both cases, therefore, its seems to be advisable
to integrate up to y and then to scale/merge up to x. <br>
So the question is: why does this happen? Would this be
related this the fact that the spot profiles gets more well
defined? In this case, is it fair to do this and to obtain
better data (and, I suppose, a better structural model)? In
principle, I suppose this might be legitimate, as even CC1/2
gets better. Has anyone else ever observed such behavior, maybe
with other processing duets? <br>
<br>
Jorge<br>
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