diff --git a/bean/model/readwrite.py b/bean/model/readwrite.py
index c1c68cd..a36493b 100644
--- a/bean/model/readwrite.py
+++ b/bean/model/readwrite.py
@@ -102,7 +102,6 @@ def write_result_table(
             )
 
     fit_df = pd.DataFrame(param_dict)
-    fit_df["novl"] = get_novl(fit_df, "mu", "mu_sd")
     if "negctrl" in param_hist_dict.keys():
         print("Normalizing with common negative control distribution")
         mu0 = param_hist_dict["negctrl"]["params"]["mu_loc"].detach().cpu().numpy()
@@ -114,6 +113,8 @@ def write_result_table(
                 .cpu()
                 .numpy()
             )
+        else:
+            sd0 = 1.0
         print(f"Fitted mu0={mu0}" + (f", sd0={sd0}." if sd_is_fitted else ""))
         fit_df["mu_scaled"] = (mu - mu0) / sd0
         fit_df["mu_sd_scaled"] = mu_sd / sd0
@@ -154,12 +155,12 @@ def write_result_table(
                 fit_df,
                 std,
                 suffix="_adj",
-                mu_adjusted_col="mu_scaled"
-                if "negctrl" in param_hist_dict.keys()
-                else "mu",
-                mu_sd_adjusted_col="mu_sd_scaled"
-                if "negctrl" in param_hist_dict.keys()
-                else "mu_sd",
+                mu_adjusted_col=(
+                    "mu_scaled" if "negctrl" in param_hist_dict.keys() else "mu"
+                ),
+                mu_sd_adjusted_col=(
+                    "mu_sd_scaled" if "negctrl" in param_hist_dict.keys() else "mu_sd"
+                ),
             )
             fit_df = add_credible_interval(fit_df, "mu_adj", "mu_sd_adj")
             if sample_covariates is not None:
@@ -168,12 +169,16 @@ def write_result_table(
                         fit_df,
                         std,
                         suffix=f"_{sample_cov}_adj",
-                        mu_adjusted_col=f"mu_{sample_cov}_scaled"
-                        if "negctrl" in param_hist_dict.keys()
-                        else f"mu_{sample_cov}",
-                        mu_sd_adjusted_col=f"mu_sd_{sample_cov}_scaled"
-                        if "negctrl" in param_hist_dict.keys()
-                        else f"mu_sd_{sample_cov}",
+                        mu_adjusted_col=(
+                            f"mu_{sample_cov}_scaled"
+                            if "negctrl" in param_hist_dict.keys()
+                            else f"mu_{sample_cov}"
+                        ),
+                        mu_sd_adjusted_col=(
+                            f"mu_sd_{sample_cov}_scaled"
+                            if "negctrl" in param_hist_dict.keys()
+                            else f"mu_sd_{sample_cov}"
+                        ),
                     )
                     fit_df = add_credible_interval(
                         fit_df, f"mu_{sample_cov}_adj", f"mu_sd_{sample_cov}_adj"
diff --git a/bean/model/run.py b/bean/model/run.py
index d09fe1e..f8bde98 100644
--- a/bean/model/run.py
+++ b/bean/model/run.py
@@ -488,3 +488,20 @@ def identify_model_guide(args):
                 fit_noise=(not args.dont_fit_noise),
             ),
         )
+
+
+def identify_negctrl_model_guide(args, data_has_bcmatch):
+    if args.selection == "sorting":
+        m = sorting_model
+    else:
+        m = survival_model
+    negctrl_model = partial(
+        m.ControlNormalModel,
+        use_bcmatch=(not args.ignore_bcmatch and data_has_bcmatch),
+    )
+
+    negctrl_guide = partial(
+        m.ControlNormalGuide,
+        use_bcmatch=(not args.ignore_bcmatch and data_has_bcmatch),
+    )
+    return negctrl_model, negctrl_guide
diff --git a/bean/model/survival_model.py b/bean/model/survival_model.py
index 9b68e2c..ddfb362 100644
--- a/bean/model/survival_model.py
+++ b/bean/model/survival_model.py
@@ -123,12 +123,12 @@ def ControlNormalModel(data, mask_thres=10, use_bcmatch=True):
     mu_alleles = pyro.sample("mu_alleles", dist.Laplace(0, 1))
     mu = mu_alleles.repeat(data.n_guides).unsqueeze(-1)
     r = torch.exp(mu)
+    with pyro.plate("rep_plate1", data.n_reps, dim=-1):
+        q_0 = pyro.sample(
+            "initial_guide_abundance",
+            dist.Dirichlet(torch.ones((data.n_reps, data.n_guides))),
+        )
     with replicate_plate:
-        with pyro.plate("guide_plate2", data.n_guides):
-            q_0 = pyro.sample(
-                "initial_guide_abundance",
-                dist.Dirichlet(torch.ones((data.n_reps, data.n_guides))),
-            )
         with time_plate as t:
             time = data.timepoints[t]
             assert time.shape == (data.n_condits,)
diff --git a/bin/bean-run b/bin/bean-run
index b4eb5f4..540bd37 100644
--- a/bin/bean-run
+++ b/bin/bean-run
@@ -31,6 +31,7 @@ from bean.model.run import (
     parse_args,
     check_args,
     identify_model_guide,
+    identify_negctrl_model_guide,
 )
 
 logging.basicConfig(
@@ -144,14 +145,8 @@ def main(args, bdata):
             run_inference(model, guide, ndata, num_steps=args.n_iter)
         )
         if args.fit_negctrl:
-            negctrl_model = partial(
-                m.ControlNormalModel,
-                use_bcmatch=(not args.ignore_bcmatch and "X_bcmatch" in bdata.layers),
-            )
-
-            negctrl_guide = partial(
-                m.ControlNormalGuide,
-                use_bcmatch=(not args.ignore_bcmatch and "X_bcmatch" in bdata.layers),
+            negctrl_model, negctrl_guide = identify_negctrl_model_guide(
+                args, "X_bcmatch" in bdata.layers
             )
             negctrl_idx = np.where(
                 guide_info_df[args.negctrl_col].map(lambda s: s.lower())