@@ -179,17 +179,18 @@
 
 The first and maybe most common one is \pythonilIdx{round}.
 This function accepts a \pythonilIdx{float} and rounds it to the nearest integer.
-If two integer numbers are equally close to it, it returns the one that is even~\cite{PSF:P3D:TPSL:BIF}.
-This rounding method is called \emph{\pgls{bankersRounding}}.
+If two integer numbers are equally close to it, it returns the one that is even~\cite{PSF:P3D:TPSL:BIF:R}.
+This rounding method is called \emph{\pgls{bankersRounding}}~\cite{PWZLZZLW2025HIOIBFPRA}.
 It is defined in the IEEE~754 standard for floating point arithmetics~\cite{IEEE2019ISFFPA} and used in many applications today, including Alipay\textsuperscript{+}~\cite{APDOC:BR}.
 
-This rounding behavior can be very surprising, as we learned in school that $x.5$ should be rounded to~$x+1$.
-Well, I learned it like this in Germany (and \pgls{Java}'s~\pythonil{Math.round} also works like that\cite{O2024JPSEJDKV2AS}), but elsewhere it may be taught differently{\dots}
+This rounding behavior can be very surprising, as we learned in school that numbers of the form~$x.5$ should be rounded to~$x+1$.
+Well, I learned it like this in Germany~(and \pgls{Java}'s~\pythonil{Math.round} also works like that\cite{O2024JPSEJDKV2AS}), but elsewhere it may be taught differently{\dots}
 Under \pgls{bankersRounding}, this will only happen with \pythonilIdx{round} if $x+1$~is even.
 The reason why \pgls{bankersRounding} is preferred is that it is not biased in any direction~\cite{MB2005RA1,SE:SO:WDP3RHTE}.
-If $x.5$ is always rounded toward~$x+1$ and we have many numbers of the form $x.5$, then the average rounded result tends to be somewhat larger than the average of the unrounded numbers.
-\Pgls{bankersRounding} avoids this problem by sometimes rounding numbers ending in~5 to larger and sometimes to smaller values.
-
+If $x.5$ is always rounded toward~$x+1$ and we have many numbers of the form~$x.5$, then the average rounded result tends to be somewhat larger than the average of the unrounded numbers.
+\Pgls{bankersRounding} avoids this problem by sometimes rounding numbers ending in~5 to larger and sometimes to smaller values.%
+%
+\begin{sloppypar}%
 Let's look at the following example.
 Assume we have a list~$S$ of numbers such that~$S=(0.5, 1.5, 2.5, 3.5, 4.5, 5.5)$.
 Traditional \inQuotes{school rounding,} which rounds up numbers ending in~.5, would give us~$S_s = (1, 2, 3, 4, 5, 6)$, as all numbers are rounded up since they all end in~0.5.
@@ -203,8 +204,9 @@
 This would also hold for the arithmetic means~\arithMean.
 This, of course, only plays a role if many values ending in~.5 appear.
 But it is quite clear why bankers would like that.
-If you work in a bank and shuffle around large sums of money, you certainly would like these sums to add up properly{\dots}
-
+If you work in a bank and shuffle around large sums of money, you certainly would like these sums to add up properly{\dots}%
+\end{sloppypar}%
+%
 We find examples of the behavior of \pythonilIdx{round} in \cref{exec:float_rounding}.
 \pythonil{round(0.4)}~yields the integer~\pythonil{0}, as expected.
 \pythonil{round(0.5)}~returns~\pythonil{0} as well, which one may not expect -- but \pythonil{0}~is even and \pythonil{1}~would be odd.