## 4 Results  
Figure 3 shows the Q-slope dataset (case studies) of the failure modes, stability study regions of the analyzed slopes, and the distribution of slope angles and heights. Based on the slope's stability and inclination angle, these are considered stable up to 45\({}^{\circ}\) (Figure 3b). Beyond 45\({}^{\circ}\), the slopes can be almost stable or unstable. From 61\({}^{\circ}\) to 90\({}^{\circ}\), slopes classified as unstable appear and persist with their maximum peak in the inclination range of 61\({}^{\circ}\) to 75\({}^{\circ}\) (Figure 3b). The comparative analysis between slope stability and height determined that slopes under five meters are stable. The \"almost stable\" definition disappeared in height ranges 41-50 m and 61-70 m. The slope failures started at 5 m and were present in all analyzed ranges, with maximum values in the 11-20 m and 51-60 m intervals (Figure 3c). Slopes were analyzed in different climatic conditions, including locations exposed to powerful elements such as water, ice, and gravity. Interestingly, almost all the values proposed by the authors [7] for Jwice were selected. In the paper, the degree of fracturing of the RQD/Jn rock mass, condition of the discontinuities (Jr/Ja), stability, and geological and environmental factors are analyzed and presented (Tables 1 and 2). The Q-slope calculations for each slope are summarized in Table 1 and plotted in Figure 5, as proposed by [PERSON] [8]. The plot shows three areas: zone of stable slopes, zone of unstable slopes, and zone of almost stable slopes (i.e., slopes with uncertain stability or when it is challenging to predict slope behavior). The slope angle and Q-slope values were plotted to locate the studied slopes in the Barton and Bar graph. Four slopes were identified in the stable zone, one in the uncertainty zone, and the remainder in the unstable zone. A confusion matrix enables a fast and easy visualization of the performance of a classification model. In a confusion matrix, the results obtained are displayed in rows and columns representing, respectively, the number of predictions of each class and the occurrences of the observed class. Three assessment sub-categories were assigned for the observed and predicted classes calculated by the Q-slope method and for the predicted class calculated with software to obtain the factor of safety (FoS): stable slopes = sub-class 1, quasi-stable = sub-class 2, and unstable or failed sloped = sub-class 3 (Table 3). A comparison of classes yields true positive, false positive, and false negative values required for calculating metrics. In this study, three confusion matrices were employed (or three analyses) to predict how the empirical method can effectively classify 30 rock slopes by comparing calculated and empirical values, data gathered in situ through direct observation, and the values calculated by software Orwa, Rocplane, and Roctopple. Figure 5: Q-slope data of the 30 analyzed slopes plotted over the slope angle and rock mass quality chart proposed by [PERSON] (2017). Refer to Table 1 for more details on the slopes. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & **Zone of** & \multirow{2}{*}{**Province**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Use**} & \multirow{2}{*}{**Higth in**} & \multirow{2}{*}{**Slep Angle**} & \multicolumn{6}{c}{**Q-Slep Value Calculation Factors**} & **Shelility** & **Value** \\  & & & &  
Statistical analysis of data revealed that of the 30 values obtained in the field (observed class), processed by the Q-slope method and processed by software (predicted class) according to the stable, almost stable, and failure sub-classes:  
* For statistical study cases a and b, 24 values are true positive values, and only 6 were classified as false positives-negatives; for statistical case c, 21 are true positive values, and 9 cases were classified as false positives-negatives.