diff --git a/src/HH4b/combine/prepare_snapshots.sh b/src/HH4b/combine/prepare_snapshots.sh
index 62383067..add6a210 100755
--- a/src/HH4b/combine/prepare_snapshots.sh
+++ b/src/HH4b/combine/prepare_snapshots.sh
@@ -1,3 +1,5 @@
+#!/bin/bash
+
 run_blinded_hh4b.sh --workspace --bfit --passbin=0
 extract_fit_result.py higgsCombineSnapshot.MultiDimFit.mH125.root "w:MultiDimFit" "inject_combined.json" --keep '*'
 if [ -f "passvbf.txt" ]; then
diff --git a/src/HH4b/combine/run_inference_upper_limits_hh4b.sh b/src/HH4b/combine/run_inference_upper_limits_hh4b.sh
index 3bbe3da9..83c47a5b 100755
--- a/src/HH4b/combine/run_inference_upper_limits_hh4b.sh
+++ b/src/HH4b/combine/run_inference_upper_limits_hh4b.sh
@@ -5,10 +5,12 @@ if [ -f "passvbf.txt" ]; then
     datacards=$card_dir/passbin3_nomasks.root:$card_dir/passbin2_nomasks.root:$card_dir/passbin1_nomasks.root:$card_dir/passvbf_nomasks.root:$card_dir/combined_nomasks.root
     datacard_names="Category 3,Category 2,Category 1,VBF Category,Combined"
     xmin="0.03"
+    parameters="C2V=0"
 else
     datacards=$card_dir/passbin3_nomasks.root:$card_dir/passbin2_nomasks.root:$card_dir/passbin1_nomasks.root:$card_dir/combined_nomasks.root
     datacard_names="Category 3,Category 2,Category 1,Combined"
     xmin="0.75"
+    parameters="C2V=1"
 fi
 model=hh_model.model_default@noNNLOscaling@noklDependentUnc
 campaign="61 fb$^{-1}$, 2022-2023 (13.6 TeV)"
@@ -16,7 +18,7 @@ campaign="61 fb$^{-1}$, 2022-2023 (13.6 TeV)"
 law run PlotUpperLimitsAtPoint \
     --version dev  \
     --multi-datacards "$datacards" \
-    --parameter-values "$masks" \
+    --parameter-values "$parameters" \
     --h-lines 1 \
     --x-log True \
     --x-min "$xmin" \
diff --git a/src/HH4b/postprocessing/datacardHelpers.py b/src/HH4b/postprocessing/datacardHelpers.py
index d1bf9c76..54c8be1f 100644
--- a/src/HH4b/postprocessing/datacardHelpers.py
+++ b/src/HH4b/postprocessing/datacardHelpers.py
@@ -34,7 +34,7 @@ class Syst:
     # in case of uncorrelated unc., which years to split into
     uncorr_years: list[str] = field(default_factory=lambda: all_years)
     pass_only: bool = False  # is it applied only in the pass regions
-    convert_shape_to_lnN: bool = False  # take shape uncertainty and conert to lnN
+    convert_shape_to_lnN: bool = False  # take shape uncertainty and convert to lnN
 
     def __post_init__(self):
         if isinstance(self.value, dict) and not (self.diff_regions or self.diff_samples):