Added clarification about sparse sampling shortcomings.

findings.md

@@ -79,10 +79,17 @@ cells. The ergodic pattern costs about 1.6 times the error here. That cost is th
 premium for insurance that pays out on the Fourier-sparse truth, where half the signal varies too
 fast for the sparse grid: there the sparse grid garbles the fast waves into false slow ones
 (this is aliasing, and is destructive; compare the sparse-grid and ergodic columns of
-figure 2). THIS CASE IS MOSTLY THEORETICAL: If we knew that there was no small-scale variation
-to capture, the sparse grid would be the right choice outright; in practice that is rarely known
-before sampling, which is exactly what motivates the signal-agnostic ergodic pattern. See
-the "Potential Future Work" section for some related thoughts.
+figure 2). THIS CASE IS MOSTLY THEORETICAL: sparse sampling's win here does not carry over to
+real applications, for two reasons. First, the rough scale of variation is always a known prior
+in practice (it is how any sample spacing gets chosen at all), but acting on that prior does not
+lead to a comfortably dense regular grid: any scale at which we knew there were no spatial
+frequencies beyond what uniform spacing resolves would be better served by sensing the same area
+with fewer, ergodically spaced samples. The regular grid's win in this column is really a
+statement that its sample budget was larger than the known field scale required. Second, for
+potential fields in particular, variation much finer than the survey scale reflects shallow
+sources rather than the deep signal of interest, which is why this ground truth is generated
+with a source-depth attenuation term. See the "Potential Future Work" section for some related
+thoughts.
 
 ![Figure 2](plt1a.png)