Diagram update; sensitivity threshold change

app.py

@@ -83,12 +83,12 @@ def main():
             }
 
             sens_input = st.sidebar.radio(label = 'Select the Sensitivity Level [OPTIONAL]',
-                                    help = 'Increasing the level of sensitivity results in more \
-                                    applications being filtered out. At the same time, this also \
-                                    increases the probability of false negatives (FNs). The rate of \
+                                    help = 'Decreasing the level of sensitivity results in less \
+                                    applications filtered out. This also \
+                                    reduces the probability of false negatives (FNs). The rate of \
                                     FNs at the lowest setting is approximately 6 percent, and \
                                     approaches 13 percent at the highest setting. \
-                                    NOTE: changing this setting does not affect the raw data in the CSV output file (only the REVIEW/REJECT labels)', 
+                                    NOTE: changing this setting does not affect the raw data in the CSV output file (only the labels)', 
                                     options = list(sens_options.keys()),
                                     index = list(sens_options.keys()).index("High"),
                                     horizontal = False)
@@ -100,7 +100,7 @@ def main():
                 """
                 This tool provides an interface for running an automated preliminary assessment of applications to the MAF call for applications.
 
-                The tool functions by running selected text fields from the application through a series of 8 LLMs fine-tuned for text classification (ref. diagram below).
+                The tool functions by running selected text fields from the application through a series of LLMs fine-tuned for text classification (ref. diagram below).
                 The resulting output classifications are used to compute a score and a suggested pre-filtering action. The tool has been tested against 
                 human assessors and exhibits an extremely low false negative rate (<6%) at a Sensitivity Level of 'Low' (i.e. rejection threshold for predicted score < 4).