diff --git a/vignettes/articles/methods.Rmd b/vignettes/articles/methods.Rmd
index 0f30c50..49be04f 100644
--- a/vignettes/articles/methods.Rmd
+++ b/vignettes/articles/methods.Rmd
@@ -199,7 +199,7 @@ sAnnual ~  Exponential(1)
 for(i in 1:nAnnual)  bAnnual[i] ~ Normal(0, sAnnual)
 
 for(i in 1:nAnnual) {
-  FemaleYearlings[i] ~ Binomial(0.5, Yearlings[i])
+  FemaleYearlings[i] ~ Binomial(sex_ratio, Yearlings[i])
   Cows[i] ~ Binomial(adult_female_proportion, CowsBulls[i])
   OtherAdultsFemales[i] ~ Binomial(adult_female_proportion, UnknownAdults[i])
   log(eRecruitment[i]) <- b0 + bAnnual[Annual[i]] + bYear * Year[i]
@@ -222,7 +222,7 @@ for(i in 1:nAnnual)  bAnnual[i] ~ Normal(0, sAnnual)
 
 for(i in 1:nAnnual) {
   Cows[i] ~ Binomial(adult_female_proportion, CowsBulls[i])
-  FemaleYearlings[i] <- round(0.5 * Yearlings[i])
+  FemaleYearlings[i] <- round(sex_ratio * Yearlings[i])
   OtherAdultsFemales[i] <- round(adult_female_proportion * UnknownAdults[i])
   logit(eRecruitment[i]) <- b0 + bAnnual[Annual[i]] + bYear * Year[i]
   AdultsFemales[i] <- max(FemaleYearlings[i] + Cows[i] + OtherAdultsFemales[i], 1)
diff --git a/vignettes/articles/priors.Rmd b/vignettes/articles/priors.Rmd
index e2dc742..16b45a0 100644
--- a/vignettes/articles/priors.Rmd
+++ b/vignettes/articles/priors.Rmd
@@ -136,7 +136,7 @@ ggplot(data = df) +
   scale_color_discrete("")
 ```
 
-The Bayesian model with vague priors (default) has annual survival estimates that closely match the Maximum Likelihood estimates, whereas the the Bayesian model with a tighter prior on the year fixed effect standard deviation results in estimates that are pulled in toward the mean.
+The Bayesian model with vague priors (default) has annual survival estimates that closely match the Maximum Likelihood estimates, whereas the Bayesian model with a tighter prior on the year fixed effect standard deviation results in estimates that are pulled in toward the mean.
 If this behaviour is desirable (i.e., to place less trust in extreme estimates), a model with a year random effect can be fit.
 
 As another example, the user might strongly believe (i.e., based on literature or biological understanding) that the true adult female proportion is 65%.