## 3 Results
### Behavior Classification  
The classification performance metrics using the RF algorithm for identifying eating, lying, lying and ruminating, standing, and standing and ruminating are presented in Tables 2-4, representing a model fit across data obtained from all temperatures versus models fit from data within each temperature. When derived and evaluated across all data, the RF had moderate accuracy for behavioral classification (67 to 78%; Table 2). This range of accuracy was similar to those fits obtained from evaluating models derived within each temperature range (Table 3). Although the specificity of models for most behaviors was generally high (69 to 100%), the sensitivity was lower for some behaviors, particularly standing and standing/ruminating behaviors (28 to 43%; Table 3). Crucially, those models derived within individual temperature ranges did not achieve similar performance when evaluated against data obtained from other temperature ranges, resulting in marked drops in precision and somewhat lower accuracy (Table 4). Figure 3: Average rectal temperature during a gradual and drastic change in temperature from six sheep throughout the experiment. Figure 2: Average heart rate of animals during a gradual and drastic change in temperature from six sheep throughout the experiment. To explore shifts in the data structure that may have contributed to the poor translatability across thermal ranges, Table 5 shows the influence of temperature range on measured behavior characteristics. Time per bout of eating, lying, and lying and ruminating was significantly affected by the thermal environment. Total time standing was significantly affected by the thermal environment, and as a reflection of that, the animals spent more time standing in the hot condition. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Performance Indicators** & **Specificity** & **Sensitivity** & **Accuracy** \\ \hline Behavior Classifier & & & \\ Eating & 94\% & 61\% & 78\% \\ Lying & 71\% & 83\% & 77\% \\ Lying and Ruminating & 89\% & 63\% & 76\% \\ Standing & 97\% & 36\% & 67\% \\ Standing and Ruminating & 100\% & 34\% & 67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Random forest algorithm performance for behavior classification of the four animals fitted with the wearable sensor across all temperatures throughout the experiment. \begin{table}
\begin{tabular}{l l c c} \hline \hline  & **Model Performance Indicators** & **Specificity** & **Sensitivity** & **Accuracy** \\ \hline \multirow{4}{*}{20 \({}^{\circ}\)C} & Behavior Classifier & & \\  & Eating & 93\% & 59\% & 76\% \\  & Lying & 69\% & 84\% & 76\% \\  & Lying and Ruminating & 89\% & 56\% & 73\% \\  & Standing & 96\% & 31\% & 64\% \\  & Standing and Ruminating & 100\% & 28\% & 64\% \\ \hline
27 \({}^{\circ}\)C & Eating & 93\% & 70\% & 82\% \\  & Lying & 75\% & 82\% & 78\% \\  & Lying and Ruminating & 89\% & 64\% & 76\% \\  & Standing & 97\% & 40\% & 68\% \\  & Standing and Ruminating & 100\% & 37\% & 68\% \\ \hline
35 \({}^{\circ}\)C & Eating & 94\% & 57\% & 75\% \\  & Lying & 75\% & 80\% & 78\% \\  & Lying and Ruminating & 87\% & 68\% & 77\% \\  & Standing & 96\% & 43\% & 70\% \\  & Standing and Ruminating & 100\% & 35\% & 67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Random forest algorithm performance for behavior classification of the four animals fitted with the wearable sensor at ambient temperatures of 20 \({}^{\circ}\)C, 27 \({}^{\circ}\)C, and 35 \({}^{\circ}\)C throughout the experiment.