## 2 Systematic Analysis Methodology
A hybrid review process was adopted, where NLP-assisted ranking of documents was complemented with manual expert assessment to ensure both efficiency and accuracy. Expert judgment was pivotal in assessing the methodological quality, applicability, and contributions of the selected studies. This meticulous process culminated in the identification and inclusion of 166 high-quality documents, which were selected based on their alignment with the key themes identified through NLP and validated by expert review. These documents form the basis for the subsequent analysis and synthesis. Quantitative Analysis: Insights Through Visualizations: In this stage, a detailed quantitative analysis was conducted using the 166 selected documents. Analytical visualizations, such as histograms, co-occurrence networks, and trend analyses, were created to uncover patterns, relationships, and temporal distributions in the dataset. These visual representations provided key insights into the distribution of topics, research trends, and collaborations, forming the basis for a data-driven understanding of the systematic review. The co-occurrence analysis of terms provided a structured representation of conceptual clusters, reinforcing the reliability of topic selection. For instance, co-citation analysis revealed sub-communities around certain specialized topics, such as multi-stage milling or sensor synergy in conveyors [28; 29], enabling the identification of emergent interdisciplinary approaches [30; 31]. Qualitative Analysis: Descriptive Review of Key Findings: Building on the quantitative insights, this stage focused on a descriptive review of the main findings from the most relevant documents among the 166 initially analyzed. The analysis emphasized the exploration of significant contributions, emerging research themes, and gaps within the studied domain. The inclusion of NLP-derived thematic groupings provided a structured framework for qualitative synthesis, ensuring that insights were extracted in a coherent and reproducible manner. By synthesizing qualitative insights, this phase provided a narrative understanding of the key outcomes, supporting a comprehensive interpretation of the systematic review. Finally, a meta-synthesis approach [32; 33] was used to converge these selected findings into practical recommendations (Section 5), illustrating how the proposed methodologies can be adopted in active copper, gold, and coal mines [34]. In Figure 2, the structured sequence of Thematic NLP Analysis + Experts is presented, illustrating how NLP techniques are combined with expert evaluations to refine thematic classification. To provide further detail on one of the core stages within the overall workflow depicted in Figure 1, this specialized procedure is outlined, consisting of multiple sub-steps, including unsupervised topic modeling, iterative expert validation, and final thematic consolidation. The process begins with the compilation of a corpus of documents, which includes all relevant studies based on predefined inclusion criteria. Then, NLP preprocessing is applied, involving tokenization and lemmatization to prepare the data for analysis. Next, topic modeling and term extraction techniques are used to identify key themes within the documents. At this point, expert validation and feedback play a crucial role in assessing and refining the generated themes to ensure domain-specific accuracy. The insights  
Figure 2: Detailed flowchart of the NLP-based thematic analysis integrated with expert evaluations. from experts are then incorporated into the process (integration of experts' insights), leading to a refined thematic categorization that aligns more closely with real-world applications. Finally, the process concludes with a final thematic structure, where validated and contextualized themes are established, forming a foundation for subsequent quantitative and qualitative analyses.