[R] help interpreting output?

[Michael F. Palopoli]

Dear R experts,

I'm hoping someone can help me to interpret the results of building 
gam's with mgcv in R.

Below are summaries of two gam's based on the same dataset.  The first 
gam (named "gam.mod") has six predictor variables.  The second gam 
(named "gam.mod2") is exactly the same except it is missing one of the 
predictor variables.  What is confusing me is the estimated defrees of 
freedom for each of the splines in the second model....

________________

 > summary.gam(mod.gam)

Family: gaussian
Link function: identity

Formula:
INT ~ s(IGS) + s(L2E) + s(TED) + s(PSD) + s(OPD) + s(GED)

Parametric coefficients:
           Estimate  std. err.    t ratio    Pr(>|t|)
constant     302.32      5.192      58.23    < 2.22e-16

Approximate significance of smooth terms:
              edf       chi.sq     p-value
s(IGS)      4.254       58.308     9.5524e-12
s(L2E)          1       8.7673     0.0030668
s(TED)          1       8.3915     0.0037697
s(PSD)          1       6.0234     0.014118
s(OPD)      2.289       12.745     0.0024349
s(GED)      3.791       152.68     < 2.22e-16

R-sq.(adj) = 0.885   Deviance explained = 91.1%
GCV score = 2124.9   Scale est. = 1617.3    n = 60

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 >summary.gam(mod.gam2)

Family: gaussian
Link function: identity

Formula:
INT ~ s(IGS) + s(L2E) + s(TED) + s(PSD) + s(OPD)

Parametric coefficients:
           Estimate  std. err.    t ratio    Pr(>|t|)
constant     302.32  4.736e-14  6.384e+15    < 2.22e-16

Approximate significance of smooth terms:
              edf       chi.sq     p-value
s(IGS)  1.757e-05   1.3524e+09     < 2.22e-16
s(L2E)   0.009991      0.21394     0.6437
s(TED)  2.945e-05   1.4913e+07     < 2.22e-16
s(PSD)  2.566e-05   6.5495e+06     < 2.22e-16
s(OPD)  5.023e-05   3.2332e+07     < 2.22e-16

R-sq.(adj) = 0.645   Deviance explained = 64.5%
GCV score = 7489.7   Scale est. = 6069.7    n = 60


________________


Any suggestions about either (1) what went wrong with the second model? 
 or (2) how the heck do I interpet these results?

Thanks,

Mike.