Index: doc/theses/mike_brooks_MMath/list.tex
===================================================================
--- doc/theses/mike_brooks_MMath/list.tex	(revision 9a35b43a09e18b63c4bf4378f46208994d288d53)
+++ doc/theses/mike_brooks_MMath/list.tex	(revision bf8112b8e07cfd3becb83c79e4e3b5eee2d064de)
@@ -1379,9 +1379,9 @@
 
 \VRef[Figure]{fig:plot-list-1ord} gives the first-order effects.
-The first breakdown, architecture/size-zone (left), shows the overall performance of all 12 experiment on the two different hardware architectures for small and medium lists (624 / 4 = 156 experiments per column).
+The first breakdown, architecture/size-zone (left), shows the overall performance of all configurations, split by the two different hardware architectures and by small \vs medium lists (624 / 4 = 156 experiments per column).
 % The relative experiment duration for each experiment is shown as a bar in each column and the black bar in that column shows the average of all 12 experiments.
 By inspection of the averages, Intel runs faster than AMD.
 Within an architecture, the small zone (lists of 4--16 elements) runs faster than the medium zone (lists of 50--200 elements).
-The overall slower execution on the AMD results from its smaller L3 cache \vs the larger cache on the Intel.
+The overall slower execution on the AMD results from its smaller cache \vs the larger cache on the Intel.
 (No NUMA effects for these list sizes.)
 Specifically, a 20\% standard deviation exists here, between the means of the four physical-effect categories.
@@ -1393,5 +1393,5 @@
 The second breakdown, use case (middle), shows the overall performance for each of the 12 use cases from \VRef[Figure]{f:ExperimentOperations} (624 / 12 = 52 experiments per column).
 % A similar situation comes from \VRef[Figure]{fig:plot-list-1ord}'s second comparison, by use case.
-While specific differences do occur, like framework X doing better on stacks than on queues, the overall range of the standard deviation of the individual use cases' means is only 9\%, indicating no unusual cases.
+ The standard deviation of the individual use cases' means is 10\%.
 A more detailed analysis occurs in the discussion of \VRef[Figure]{fig:plot-list-2ord}.
 % But they are so irrelevant to the issue of picking a winning framework that it is sufficient here to number the use cases opaquely.
@@ -1401,10 +1401,10 @@
 The third breakdown, framework (right), shows the overall performance of the 4 list implementations (624 / 3.25 = 192).
 Here, \CFA runs similarly to \uCpp and LQ-@list@ runs similarly to @tailq@.
-The standard deviation of the frameworks' means is 8\%.
+The standard deviation of the frameworks' means is 7\%.
 % Framework choice has, therefore, less impact on your speed than the lottery tickets you already hold.
 Now, \CFA/\uCpp run slower than LQ-@list@/@tailq@ by 15\%, a fact explored further in \VRef{s:SweetSoreSpots}.
 But so too does use case X typically beat use case II by 38\%.
 As does a small size on the Intel typically beat a medium size on the AMD by 66\%.
-Hence, architecture and usage patterns have a significant affect on the specific framework.
+Hence, architecture and usage pattern have a more significant effect on speed than the selection of a framework.