## 3 Results
### Description of Pre-Slaughter Conditions and Observations, and Prevalence of carcass Skin Lesions
The number batches with at least one carcass with severe middle lesions was 57, 27 of which had more than 10% of carcass with severe middle lesions. Thirty-six batches showed at least one carcass with severe shoulder lesions, and 16 of them had more than 10% of carcass with severe shoulder lesions. Correlations among all variables annotated at T1 and T2 and the prevalence of carcass scored with the DMRI scale are reported in File S2 in the Supplementary Material. The results of the correlation analysis indicated that batches having more carcasses with the rear region scored as 1 tended to also have more carcasses with superficial lesions in the middle and shoulder regions. On the other hand, batches that had a higher prevalence of carcasses scored as 2 or 3 for the three regions were also those with higher frequencies of events such as pigs falling and overlapping at T1; with more animals showing ear, side, back, and thigh discoloration at T2; with more pigs vocalizing at T2; and with a higher frequency of negative human-animal interactions at T2 (both hit with plastic tube and shouts, as those two events were highly correlated, with r = 0.98, \(p<0.001\)). Association between Pre-Slanghter Conditions and Severe Lesions in the Rear Region of Heavy Pig Excesses  
The results of the univariable models for the prevalence of severe rear lesions are reported in Table S2. In particular, the prevalence of severe rear lesions was associated with longer waiting before unloading (b = 0.058, \(p=0.0004\)), increased total number of human-animal events at T1 (b = 0.066, \(p=0.009\)), ear discoloration at T2 (b = 0.071, \(p=0.001\)), back discoloration at T2 (b = 0.028, \(p=0.009\)), shouts at T2 (b = 0.116, \(p=0.014\)), hit with plastic tube at T2 (b = 0.109, \(p=0.023\)), falling at T2 (b = 0.356, \(p=0.021\)). The results of the final multivariable regression model for the prevalence of severe lesion in the rear region of the carcass are graphically displayed with a forest plot in Figure 2a. The \(x\)-axis reports the standardized estimate effect of the factors included in the final multivariable regression model after stepwise backward selection. An increased prevalence in rear lesion severity was related to batches that waited longer before being unloaded (\(p=0.0002\)) and were placed in lairage pens with higher space allowance per animal (\(p=0.010\)), with higher percentages of pigs showing panting at T1 (\(p=0.0004\)), with more animals displaying ear discoloration at T2 (\(p=0.0006\)), and with more frequent negative human-animal interactions at T2 (hit with plastic tube and shouts; \(p=0.003\)). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variables** & **Mean** & **Median** & **S.D.** & **Min** & **Max** \\ \hline Middle & & & & & \\
1 (\%) & 52.05 & 47.92 & 27.77 & 8.70 & 100.00 \\
2 (\%) & 36.58 & 39.13 & 19.71 & 0.00 & 73.91 \\
3 (\%) & 10.06 & 6.44 & 10.46 & 0.00 & 39.13 \\
4 (\%) & 1.31 & 0.00 & 3.41 & 0.00 & 16.67 \\ Moderate lesions (1 + 2) & 88.63 & 91.49 & 12.07 & 52.17 & 100.00 \\ Severe lesions (3 + 4) & 11.37 & 8.51 & 12.07 & 0.00 & 47.83 \\ Shoulder & & & & & \\
1 (\%) & 72.94 & 78.26 & 21.52 & 21.74 & 100.00 \\
2 (\%) & 20.23 & 17.39 & 14.31 & 0.00 & 56.52 \\
3 (\%) & 4.94 & 0.00 & 7.63 & 0.00 & 30.43 \\
4 (\%) & 1.90 & 0.00 & 3.97 & 0.00 & 16.67 \\ Moderate lesions (1 + 2) & 93.17 & 98.00 & 10.38 & 60.87 & 100.00 \\ Severe lesions (3 + 4) & 6.83 & 2.00 & 10.38 & 0.00 & 39.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._Figure 2b reports the regression tree plot displaying the nodes affecting the most the variability observed among batches in the prevalence of carcasses with severe lesions in the rear region. Each rectangle reports the sum of the prevalence of carcasses with severe rear lesions for the batches remaining at each split step and the percentage of batches remaining at each split. Four variables remained in the classification obtained with regression tree analysis, generating four splitting nodes.