Random effects correlations and factor reference levels

I am fitting mixed effects models where there is a random effect which  
acts differently on each level of a factor, much as in the Machines  
example from the nlme package:

library(nlme)

M1 <- lme(fixed = score ~ Machine, random = ~Machine|Worker,  
data=Machines)
VarCorr(M1)

Worker = pdLogChol(Machine)
            Variance   StdDev    Corr
(Intercept) 16.6405306 4.0792806 (Intr) MachnB
MachineB    34.5466908 5.8776433  0.484
MachineC    13.6150244 3.6898543 -0.365  0.297
Residual     0.9246296 0.9615766

I was interpreting these correlations to mean that the estimate of  
workers? scores on Machine B (or more precisely their BLUPS, interpreted  
as something like their residuals from the average on Machine B) are  
weakly positively correlated with their scores on Machine C (r=0.297).  
Because Machine A is the reference level of the factor ?Machine? (with  
treatment contrasts), I was also interpreting the ?(Intercept)? to  
represent Machine A, i.e. that the scores of the workers on Machine A  
are somewhat positively correlated with their scores on Machine B  
(r=0.484), but negatively with their scores on Machine C (r=-0.365).

If this were true, I would expect these conclusions to be robust to  
exactly which machine is the reference level in the contrasts for the  
Machine factor, but apparently they are not:

Machines$Machine <- relevel(Machines$Machine, ref = "B")
M2 <- update(M1)
VarCorr(M2)

Worker = pdLogChol(Machine)
            Variance   StdDev    Corr
(Intercept) 74.3956398 8.6252907 (Intr) MachnA
MachineA    34.5466902 5.8776433 -0.910
MachineC    35.2950235 5.9409615 -0.882  0.805
Residual     0.9246296 0.9615766

What is going on here/what am I misinterpreting?

As expected, these two models appear to be equivalent by anova(M1,M2),  
yet the correlation among workers between machines A and B appears to be  
strongly negative in the second case (r = -0.91), where it seems  
moderately positive in the first case (r=0.484) and intervals() on the  
two models suggests no overlap of (admittedly pretty broad) confidence  
intervals in this case. Add to that the differences in the estimated  
variance components/standard deviations ? each model has an identical  
residual, but very different individual values (that for Machine C going  
from 13.6 to 35) and totals; at the same time the estimate for the  
variance among workers on Machine B in model M1 (34.5466908)  is  
suspiciously similar (albeit not quite identical) to the estimate for  
variance among workers on Machine A in model M2 (34.5466902).

The values are pretty much the same fitting with lmer (though in  
practice I need the correlation and weights arguments available with  
lme), which makes it feel like a problem with my interpretation. Or is  
it really just a case of too little data to resolve such ?subtle  
parameters clearly?

Dropping the fixed effect doesn?t make things much better and, on the  
models I am running in anger, there is more data and the differences are  
even more pronounced. It looks a bit like this issue:  
http://stats.stackexchange.com/questions/82102/changing-reference-level-for-contrasts-changes-results-in-r-3-0-2-lme4-1-1-2-vs
which was framed in terms of versions but, given that I get very similar  
issues using nlme and lme4, it doesn?t seem likely to be a version issue  
here (albeit I don?t have old versions immediately to hand to test this  
on).

Any insights much appreciated,

Chris

running nlme_3.1-122 in R 3.2.2 on OS X 10.10.5


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