From 8cce903a981c004502d1fdf29a650fc58f72042d Mon Sep 17 00:00:00 2001
From: mjkwiatkowski <mati.rewa@gmail.com>
Date: Mon, 20 Jul 2026 12:43:25 +0200
Subject: git amend

---
 content/conclusion.tex | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'content/conclusion.tex')

diff --git a/content/conclusion.tex b/content/conclusion.tex
index 86d9f68..b7694c8 100644
--- a/content/conclusion.tex
+++ b/content/conclusion.tex
@@ -70,7 +70,7 @@ To achieve the \gls{nasem} goals of digital twinning~\cite{DBLP:usdoe/report/AP2
 	      A \gls{dcdt} must posses predictive capabilities, by definition~\cite{DBLP:usdoe/report/AP26894}.
 	\item \textbf{What Is Missing?}\\
 	      To power the predictions, we envision an \gls{ml}-based inference engine as a necessary component of digital twinning.
-	      The need for \gls{ml} arises naturally in scenarios where large volumes of data, requiring little to no preprocessing meet the demand for estimating future facility behaviour~\cite{Wikipedia:PredictiveModelling,CambridgeUniversityPress:book/Deisenroth}.
+	      The need for \gls{ml} arises naturally in scenarios where large volumes of data, requiring little to no preprocessing meet the demand for estimating future facility behaviour~\cite{Wikipedia:article/PredictiveModelling,CambridgeUniversityPress:book/Deisenroth}.
 	      However, currently there are no \gls{dcdt} deployments that model the warehouse using an \gls{ml} approach to predict events (see \Cref{tab:dt_features_comparison}).
 	\item \textbf{The Next Steps}\\
 	      In short, we stipulate \gls{dcdt}s should include \gls{ml} in their \gls{oda} analysis.
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