Abstract Continuous assessment of slope stability is important to the open pit design and operation. This article aims to present a tool for evaluating the stability conditions of rock slopes in mining, based on a global geotechnical database, using machine learning techniques. Different models are evaluated in this research: the general model, which uses all variables; the mathematical model, which uses only variables selected by the random forest (out-of-bag); and two expert-based models: the Q-Slope model and the Santos model. The validation of the model was done through the test sample, using partition confusion matrices aiming at reproducibility of the results. A study of the types of errors was carried out using Principal Component Analysis (PCA). The study of errors allowed the identification of samples that were inconsistent with the others. Afterwards, the models were redone and compared with the previous ones. The best performers are presented and discussed. The proposed methodology does not replace the classic analysis of slope stability. On the contrary, it contributes to engineers and geologists with a tool for monitoring the stability conditions of slopes in a mining operation. Slope stability analysis must be carried out throughout the mine's lifetime and, therefore, it is believed that the tool proposed here can optimize the selection of slopes most susceptible to instability.

ABSTRACT Repeated application of pesticides disturbs microbial communities and cause dysfunctions on soil biological processes. Granstar® 75 DF is one of the most used sulfonylurea herbicides on cereal crops; it contains 75% of tribenuron-methyl. Assessing the changes on soil microbiota, particularly on the most abundant bacterial groups, will be a useful approach to determine the impact of Granstar® herbicide. For this purpose, we analyzed Actinobacteria, which are known for their diversity, abundance, and aptitude to resist to xenobiotic substances. Using a selective medium for Actinobacteria, 42 strains were isolated from both untreated and Granstar® treated soils. The number of isolates recovered from the treated agricultural soil was fewer than that isolated from the corresponding untreated soil, suggesting a negative effect of Granstar® herbicide on Actinobacteria community. Even so, the number of strains isolated from untreated and treated forest soil was quite similar. Among the isolates, resistant strains, tolerating high doses of Granstar® ranging from 0.3 to 0.6% (v/v), were obtained. The two most resistant strains (SRK12 and SRK17) were isolated from treated soils showing the importance of prior exposure to herbicides for bacterial adaptation. SRK12 and SRK17 strains showed different morphological features. The phylogenetic analysis, based on 16S rRNA gene sequencing, clustered the SRK12 strain with four Streptomyces type strains (S. vinaceusdrappus, S. mutabilis, S. ghanaensis and S. enissocaesilis), while SRK17 strain was closely related to Streptomyces africanus. Both strains were unable to grow on tribenuron methyl as unique source of carbon, despite its advanced dissipation. On the other hand, when glucose was added to tribenuron methyl, the bacterial development was evident with even an improvement of the tribenuron methyl degradation. In all cases, as tribenuron methyl disappeared, two compounds were detected with increased concentrations. These by-products appeared to be persistent and were not degraded either chemically or by the studied strains. Based on these observations, we suggested that bacterial activity on carbon substrates could be directly involved in the partial breakdown of tribenuron methyl, by generating the required acidity for the first step of the hydrolysis. Such a process would be interesting to consider in bioremediation of neutral and alkaline tribenuron methyl-polluted soils.