Slope Stabilization Systems for Accident Prevention: A Case Study of the Expansion and Improvement of the Huaruro Countryside, Arequipa
Bibliographic record
Abstract
The objective of the research was to implement slope stabilization systems to prevent accidents in the expansion and improvement project of Campiña Huaruro, province of Caylloma, Arequipa.The methodology used was of pre-experimental design, where slope safety was evaluated within the operational jurisdiction of a mining company in high-risk sectors such as Belen, Paclla, Malata and Huaruro, based on risk maps and detailed observation.The results indicated that the systems implemented were cable-reinforced meshes for dacite rock and dynamic rockfall barriers designed for the type of andesite rock present in the project slopes.In addition, the IPERC revealed that the risk levels for the tasks performed on the slopes were 25% moderate risk and 75% low risk.As a result of these preventive measures, a reduction in the accident rate was achieved in the Huaruro project, decreasing from 17.36 to 5.20, which represents a difference of 12.16.In conclusion, the implementation of the slope stabilization system proved to be highly effective in the prevention of occupational accidents in the Huaruro expansion and improvement project.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".