## 5 Discussion  
There is a clear correlation between slopes visualized as stable and those calculated as stable using the Q-slope. Slope no. 2, 3, 6, and 7, classified as \"stable\", are also classified as \"stable\" according to the graph proposed in 2017 for the Q-slope. It should be mentioned that slope no. 2 and 3 have been controlled for seven consecutive years because of their geographical location and access,t exhibiting no evidence of instability during the monitoring period. Slope no. 5 was classified in the field as \"not very stable\" and is in the zone of \"Quasi-Stable\" slopes in the Q-slope graph [8]. However, for the remaining 25 slopes, there are slight differences between what is predicted by the Q-slope method and what is observed. Slope no. 13, 15, 19, 20, 21, and 22 were classified in the field as \"Quasi-Stable\". However, they were located in the \"unstable slope\" graph area. The original graph [8] seems conservative and on the safe side for these slopes (Figure 6), which is very reasonable for empirical and pre-design analysis. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Statistical Case a: Predicted Class (Q-slope) Compared to Observed Class**} \\ \multicolumn{1}{c}{**Sub-Class**} & \multicolumn{1}{c}{**n (Truth Overall)**} & \multicolumn{1}{c}{**n (Classified)**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F1 Score**} & \multicolumn{1}{c}{**Overall Accuracy**} \\ \hline
1 & 4 & 9 & 83.33\% & 0.44 & 1.00 & 0.62 & \\
2 & 1 & 0 & 96.67\% & 0.00 & 0.00 & 0.00 & 80\% \\
3 & 25 & 21 & 80.00\% & 0.95 & 0.80 & 0.87 & \\ \hline \multicolumn{6}{c}{**Statistical Case c: Predicted Class (FoS) Compared to Observed Class**} \\ \multicolumn{1}{c}{**Sub-Class**} & \multicolumn{1}{c}{**n (Truth Overall)**} & \multicolumn{1}{c}{**n (Classified)**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F1 Score**} & \multicolumn{1}{c}{**Overall Accuracy**} \\ \hline
1 & 4 & 9 & 83.33\% & 0.44 & 1.00 & 0.62 & \\
2 & 7 & 0 & 76.67\% & 0.00 & 0.00 & 0.00 & 70\% \\
3 & 19 & 21 & 80.00\% & 0.81 & 0.89 & 0.85 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the confusion matrices for the three statistical study cases, considering data observed in the field, calculated with Q-slope and calculated with software (Factor of safety-FoS). Considering the data obtained for slopes 13, 15, 19, 20, 21, and 22 in the present work, an extension to the area of \"Slope stability uncertain\" is proposed, and the door is also left open for new studies to determine the possible projection from the proposed area towards the lower part of the \"Unstable slopes-slope stability un-certain\" zone. The kinematic analysis does not consider rock mass quality but analyzes the equilibrium of rigid blocks that destabilize through one or more surfaces and slip on each other. In the 30 analyzed cases, failures occurred through discontinuities, and kinematic analysis was the best representation of reality. Although the friction angle and joint directions are not considered in the equation proposed by [PERSON] and [PERSON] (2015) (Equation (1)), these are necessary to validate the faults identified in situ, related to joint orientation and how slope faults are produced. This analysis enables the selection of a suitable O-factor, essential for Q-slope calculation. Based on the kinematic analysis, the amount of joint sets present can be ratified, and therefore, the value of Jn that represents the analyzed slope can be defined. The main issue encountered when using the Q-slope index is the precise determination of the O-factor in those slopes affected by toppling failures. In addition, this O-factor is highly biased when quantifying slopes affected by toppling ruptures. The authors of the Q-slope method determined that applying the O-factor to toppling cases could consider the same criteria of planar and wedge cases.