final fixes

thesis/sections/appendix.tex

@@ -21,7 +21,7 @@ and after-OV (0.0833) all occurring at 20 days.
 The \textbf{Convolutional Transformer} performs best overall at 40 days and best before ovulation at 160 days (MSE 0.0286).
 See Table~\ref{tab:ovover_windows_compact_mse} for a summary.
 
-\begin{table}[t]
+\begin{table}[htbp]
     \small
     \renewcommand{\arraystretch}{1.15}
     \setlength{\tabcolsep}{6pt}
@@ -68,7 +68,7 @@ with lowest before-OV MSE at 12/day (0.0255) and after-OV MSE at 288/day (0.0578
 Results are summarized in Table~\ref{tab:ovover_resolution_compact_mse};
 full resolution grids are in Appendix Table~\ref{tab:ov_over_results_by_resolution}.
 
-\begin{table}[t]
+\begin{table}[htbp]
     \small
     \renewcommand{\arraystretch}{1.15}
     \setlength{\tabcolsep}{6pt}
@@ -110,7 +110,7 @@ Finally, the \textbf{Convolutional Transformer} achieves its lowest overall and
 Table~\ref{tab:ovover_params_compact_mse} summarizes these parameter-dependent results;
 full comparisons are included in Appendix Tables~\ref{tab:ov_over_results_by_model_parameters_lstm}–\ref{tab:ov_over_results_by_model_parameters_conv_transformer}.
 
-\begin{table}[t]
+\begin{table}[htbp]
     \scriptsize
     \renewcommand{\arraystretch}{1.15}
     \setlength{\tabcolsep}{6pt}
@@ -198,7 +198,7 @@ As before, all learned models outperform the baselines by a wide margin.
 The full table with MSE and MAE for all models can be found in the appendix, Table~\ref{tab:regular_vs_irregular_ov_over_results}.
 
 
-\section{Extra Figures}\label{app:figs}
+\section{Extra Tables}\label{app:tabs}
 
 \begin{landscape}
     \begin{table}

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.

thesis/sections/methodology.tex

@@ -286,8 +286,8 @@ Then each input token at time \( t \in \{1, \dots, T\} \) is:
 The full input sequence is then represented as a matrix:
 \[
     X = \begin{bmatrix}
-            x_1 \\
-            x_2 \\
+            x_1    \\
+            x_2    \\
             \vdots \\
             x_T
     \end{bmatrix}
@@ -525,11 +525,11 @@ and whether incorporating data from previous cycles improves learning or introdu
         Window length      & Historical context in days (input window size) & 10, 20, 40, 80, 160          \\
         \bottomrule
     \end{tabular}
-    \caption{Input-related hyperparameters for LSTM and Transformer models.}
+    \caption{\parbox{\linewidth}{Input-related hyperparameters for LSTM and Transformer models.}}
     \label{tab:input_hyperparameters_basic}
 \end{table}
 
-\begin{table}[ht]
+\begin{table}[htbp]
     \centering
     \begin{tabular}{l>{\raggedright\arraybackslash}p{0.45\linewidth}>{\raggedright\arraybackslash}p{0.3\linewidth}}
         \toprule
@@ -897,4 +897,12 @@ Explicit fairness evaluations and diverse validation cohorts are essential befor
 work is a medical device company, which underscores the need to guard against commercial bias.
 Results should be independently validated, and any translation into clinical or consumer use must be preceded by prospective,
 peer-reviewed trials.
-Without such validation, deploying fertility prediction tools risks undermining trust and causing harm. \\
+Without such validation, deploying fertility prediction tools risks undermining trust and causing harm.
+
+
+\section{Code Availability}\label{sec:code_availability}
+The complete source code and LaTeX files used for this thesis are available at:\\
+\small
+\url{https://gitlab.com/blankinator/temperature-based-fertility-prediction-thesis}\\
+\normalsize
+The repository is archived under the release v1.1-thesis-final.

thesis/sections/results.tex

@@ -45,7 +45,7 @@ The \textbf{Convolutional Transformer} achieves the lowest overall MSE (0.0041)
 A summary of best-performing window lengths per architecture is shown in Table~\ref{tab:fertility_windows_compact_mse};
 full results including MAE are provided in Appendix Table~\ref{tab:fertility_results_by_window_length}.
 
-\begin{table}[t]
+\begin{table}[htbp]
     \small
     \renewcommand{\arraystretch}{1.15}
     \setlength{\tabcolsep}{6pt}
@@ -100,7 +100,7 @@ though higher resolutions can be advantageous for detecting short-term fertile-d
 Results are summarized in Table~\ref{tab:fertility_resolution_compact_mse}; full metrics are in Appendix Table~\ref{tab:fertility_results_by_window_resolution}.
 
 
-\begin{table}[t]
+\begin{table}[htbp]
     \small
     \renewcommand{\arraystretch}{1.15}
     \setlength{\tabcolsep}{6pt}
@@ -148,7 +148,7 @@ Detailed comparisons are shown in Table~\ref{tab:fertility_params_compact_mse},
 with full results in Appendix Tables~\ref{tab:fertility_results_by_model_parameters_lstm}–\ref{tab:fertility_results_by_model_parameters_conv_transformer}.
 
 
-\begin{table}[t]
+\begin{table}[htbp]
     \scriptsize
     \renewcommand{\arraystretch}{1.15}
     \setlength{\tabcolsep}{6pt}