Q:

Two intercepts for Tobit regression in R using VGAM

I'm fitting a Tobit regression left censored at 0 using the vglm function from the VGAM package. But, after fitting the model, I'm getting two intercepts? Is this a problem with the function? If not, how do I interpret the two coefficients? Here is my function call:
model <- vglm(log_load_smoothed ~ treatment1 + treatment2,
              family = tobit(Lower = 0), 
              data = clients[nonemail_holdout == 0])

And the output is:
Call:
vglm(formula = log_load_smoothed ~ treatment1 + treatment2, family = tobit(Lower = 0), 
    data = clients[nonemail_holdout == 0])

Pearson residuals:
              Min       1Q   Median       3Q   Max
mu       -18.2011 -0.01683 -0.01514 -0.01482 19.83
loge(sd)  -0.0668 -0.06317 -0.05865 -0.05779 22.85

Coefficients: 
                 Estimate Std. Error z value Pr(>|z|)    
(Intercept):1   -36.39145    0.66122 -55.037  < 2e-16 ***
(Intercept):2     2.59319    0.01717 150.991  < 2e-16 ***
treatment1Save    0.38739    0.20983   1.846  0.06486 .  
treatment1Offer   1.20873    0.20372   5.933 2.97e-09 ***
treatment2Save    0.05581    0.20620   0.271  0.78666    
treatment2Offer   0.52358    0.20229   2.588  0.00965 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Number of linear predictors:  2 

Names of linear predictors: mu, loge(sd)

A:

It's the intercept for the second linear predictor which are described at the end of the model output. The first linear predictor describes $\mu$, the expectation of the latent uncensored normal variable. The second linear predictor describes $\log(\sigma)$, i.e., the standard deviation of the latent uncensored normal variable using a log link.
This is briefly stated at the end of the summary output: Names of linear predictors: mu, loge(sd).
In short: The first intercept is the usual intercept of the tobit model. The second intercept is the log-standard deviation of the latent variable.

