diff --git a/R/sim.cs.R b/R/sim.cs.R
index 9582a82c..96e77d12 100644
--- a/R/sim.cs.R
+++ b/R/sim.cs.R
@@ -26,8 +26,7 @@
 #' - `r`: shape factor of antibody decay
 
 #' @param predpar an [array()] containing MCMC samples from the Bayesian distribution of longitudinal decay curve model parameters. NOTE: most users should leave `predpar` at its default value and provide `curve_params` instead.
-#' @param noise_limits noise distribution parameters
-#' @param baseline_limits baseline noise distribution parameters
+#' @param noise_limits biologic noise distribution parameters
 #' @param npar number of longitudinal curve parameters in model
 #' @param ... additional arguments passed to `simcs.tinf()`
 #' @return a [dplyr::tibble()] containing simulated cross-sectional serosurvey data, with columns:
@@ -52,15 +51,18 @@ sim.cs <- function(
       prep_curve_params_for_array() %>%
       df_to_array(dim_var_names = c("antigen_iso", "parameter")),
     noise_limits,
-    baseline_limits,
     npar,
     ...)
 {
 
   stopifnot(length(lambda) == 1)
 
+  day2yr = 365.25
+
   ablist = 1:length(antigen_isos)
 
+  baseline_limits <- noise_limits
+
   ysim <- simcs.tinf(
     lambda = lambda,
     n.smpl = n.smpl,
@@ -86,6 +88,6 @@ sim.cs <- function(
   }
   colnames(ysim) <- c("age", antigen_isos)
 
-  return(ysim |> as_tibble())
+  return(ysim |> as_tibble() %>% mutate(age = round(age/day2yr, 2)))
 
 }
diff --git a/vignettes/articles/simulate_xsectionalData.Rmd b/vignettes/articles/simulate_xsectionalData.Rmd
index 3334ecbb..602ce774 100644
--- a/vignettes/articles/simulate_xsectionalData.Rmd
+++ b/vignettes/articles/simulate_xsectionalData.Rmd
@@ -66,16 +66,13 @@ ab.nm <- rbind(c("HlyE","IgA"),c("HlyE","IgG"));
 # antibodies <- dmcmc$antigen_iso |> droplevels() |> levels()
 antibodies = c("HlyE_IgA", "HlyE_IgG")
 abs <-  c(1,2);
-age.fx <- NA; # age used for longitudinal model (fixed for now)
-day2yr <- 365.25;
 lambda <- 0.2; # incidence (1/yr)
 npar <- 5; # y0, y1, t1, alpha, shape
 
 lifespan <- c(0,20); # range covered in simulations
 nrep <- 100; # cross-sectional sample size
 
-dlims <- rbind(c(1e-2,5e-1),c(1e-2,5e-1)); # noise distribution
-blims <- dlims; # baseline distribution
+dlims <- rbind(c(1e-2,5e-1),c(1e-2,5e-1)); # biologic noise distribution
 
 csdata0 <- sim.cs(
   curve_params = dmcmc,
@@ -88,12 +85,10 @@ csdata0 <- sim.cs(
   renew.params = TRUE,
   add.noise = TRUE,
   noise_limits = dlims,
-  baseline_limits = blims,
   npar = npar
 )
 
 
-csdata0[, 1] <-  csdata0[, 1] / day2yr
 
 ```
 
@@ -126,7 +121,6 @@ sim.cl <-
       npar = npar
     )
     
-    csdata[, 1] <-  csdata[, 1] / day2yr
     
     csdata1 <- csdata %>% 
       as.data.frame() %>% 
@@ -157,7 +151,7 @@ csdataL <- as.data.frame(csdata0) %>%
 est.incidence(csdataL %>%
                 rename(y = value,
                        a = age), 
-              dmcmc, cond, antigen_isos = c("HlyE_IgA"))
+              dmcmc, cond, antigen_isos = c("HlyE_IgG", "HlyE_IgA"))
 
 
 ```