final fixes

thesis/sections/background.tex

@@ -593,7 +593,7 @@ In practice, convolutional architectures have achieved strong performance on seq
 For example,~\citeauthor{lecun_convolutional_1998} showed that a simple Temporal Convolutional Network often outperforms canonical
 recurrent models (like LSTMs) across diverse sequence modeling benchmarks.
 Their experiments suggest that CNNs are “a natural starting point for sequence modeling,”
-especially when temporal features are local or multi-scale.
+especially when temporal features are local or multiscale.
 In summary, 1D convolutions provide an efficient way to extract local temporal features and compress high-resolution sequences,
 complementing recurrent and attention-based models in time-series analysis
 

thesis/sections/introduction.tex

@@ -12,7 +12,7 @@ At the same time, many individuals seek alternatives to hormonal contraception,
 Accurate fertility prediction thus carries implications not only for individual reproductive autonomy,
 but also for public health, demographic trends, and the development of safe, data-driven fertility support tools.
 
-While textbooks often describe a menstrual cycle as lasting 28 to 30 days with ovulation around day 14~\cite{owen_physiology_nodate},
+While textbooks often describe a menstrual cycle as lasting 28 to 30 days with ovulation around day 14~\cite{owen_physiology_1975},
 such regularity is the exception rather than the rule~\cite{munster_length_1992, bull_real-world_2019}.
 For individuals with consistent cycle patterns, simple calendar-based predictions may suffice.
 However, for the majority, especially with increasing age and associated irregularity, more sophisticated methods are necessary.