## 6 Conclusions  
Rock mass classifications have been successfully used worldwide for rock slope characterization for multiple reasons: effectiveness due to their connection with empirical methods and dissemination and communication across scientists, engineers, and the general public. In other words, rock mass classifications \"put numbers to geology\" [35]. The Q-slope method was applied herein in three natural regions of Ecuador--coast, mountains, and jungle--where igneous, sedimentary, and metamorphic rocks exhibiting different weathering conditions were found. The result of the stability analysis using the Q-slope method determined that of the 30 slopes analyzed, 25 are in unstable conditions, 1 is almost stable, and 4 slopes are in stable conditions. The results demonstrated that instability processes occur for slopes starting at 61 degrees of inclination and 6 m high. One of the main modifications implemented in the Q-method for its application to slopes is the shear strength, which, in our specific case, is obtained by multiplying the Jr/J ratio by the new O-factor. Nevertheless, when slopes that present toppling failures are studied, the method does not clearly establish the values to be considered for the O-factor (differently from the cases of planar and wedge failures). Therefore, future investigation is advisable to determine the O-factor values rigorously and accurately, especially for the toppling cases. The parameter could depend on the difference in strikes and dips between slope and joints, following the determination of the F1, F2, and F3 factors of the SMR correction. Considering the values obtained and the metrics calculated for each class of statistical case a, it can be concluded that as sub-class 1 presents high values for precision and recall, the method can perfectly classify this sub-class. For sub-class 2, a high precision value is obtained with a low recall value; in this case, the method does not classify this class very well--however, when it does, it is highly reliable. For sub-class 3, the model provides a low precision value and a high recall value, which suggests that the method classifies the sub-class well but can also confuse the individuals of other sub-classes. The F1 score values obtained for each sub-class in the different statistical cases analyzed present values that range from 0.62 to 1, indicating that the Q-slope method is a balanced method that can confuse some cases as false but is also capable of correctly classifying all positive true cases, such as those of sub-class 1 of statistical case a. Only sub-class 2 of statistical case a returned an F1 score value of 0.25, which means that the model can confuse values as false for this specific case. Sub-classes 2 for statistical cases b and c present null values because the calculated FoS is a fixed value that only defines if the slope is stable or unstable. In these cases, a direct association could not be made between the quasi-stability obtained in situ and that predicted by the empirical method and FoS. In general, considering that overall accuracy can be divided into quartiles, classifying quartile C1 as bad, C2 as intermediate, C3 as good, and C4 as very good, the value obtained for OA herein occupies the highest quartile, ranking the method as very good. The results demonstrated the applicability and reliability of the Q-slope method in Ecuador. This Andean country can be considered a natural laboratory for geosciences because of its diversity in geological, geomorphological, and environmental conditions. Conceptualization, C.B.B., L.J. and R.L. ; methodology, C.B.B. and L.J. ; validation, C.B.B., L.J., R.L., R.T., M.C. and A.R. ; formal analysis, L.J., R.T. and C.B.B. ; investigation, C.B.B., L.J. and R.L. ; writing--original draft preparation, C.B.B., L.J., R.L., R.T., M.C. and A.R. ; writing--review and editing, C.B.B. and L.J. ; supervision, C.B.B., L.J., R.L., R.T., M.C. and A.R. All authors have read and agreed to the published version of the manuscript.