Impacto de la transformación digital en la minería subterránea peruana
Bibliographic record
Abstract
Currently, an evident change has been noticed regarding the digital transformation of production processes as a result of the fourth industrial revolution, as well as COVID-19, which have shown how remote work and process optimization greatly influence in productivity. The mining sector is no exception, since in countries like Chile, Europe and Canada, they have automated operations, avoiding risks and damage to the health of workers. Thus, underground mining, being a method widely disseminated and applied in mining activity in Peru, presents challenges compared to the traditional method, so great technological advances are not yet evident. That is why this research is a study of the impact of digital transformation on the productivity of Peruvian underground mining, for which the main advantages and disadvantages of this type of mining in Peruvian territory were diagnosed; The challenges of underground technologies so that they can be used were examined and a case study was analyzed to establish the impact of the technology. In addition, it aims to improve the productivity of the company studied. The hypothesis raised in this analysis is that the implementation of technology focused on digital transformation increases productivity in underground mining. Based on the case study, it is concluded that underground mining, within the NEXA company, has noticed how the digital transformation has represented a control and increase in production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".