Estimating the Economic Impact of Mining Accidents: A Case Study from Peru
Bibliographic record
Abstract
The objective of this research was to evaluate the real costs of accidents and their impact on the management of a mining company, (Case: Fatal accident of a worker and the total loss of a backhoe), the causes that originated this study is that the vast majority of companies do not technically analyze all the costs involved as production loss due to the stoppage of the company's activities (costs of machinery and equipment rental, food and stay of workers, fines stipulated by law, compensation, loss of image, among others), whose consequence is the ignorance of the real amount of the loss generated by the accident.The previous studies were carried out with the Simonds Method or Average Costs, which improves Heinrich's study, and gives us an equation to calculate the costs: CT = CS + (CPi x Ai) + Ce, which was surpassed by Frank Bird's method, by means of his famous iceberg of accident costs, however these were carried out in factories in the United States, but the norms and laws of that country do not agree with those of Peru, which is why taking into account all this and adapting it to those of Peru, The present research work has been carried out, in which other variables have been considered, such as losses in the process, costs caused by work stoppages, collaborators' salaries, machinery stopped due to sanctions by the corresponding ministry (MEM), and miscellaneous costs; From the comparative result with the Frank Bird method, the sum of $980,000 USD has been calculated, while with the Frank Bird study the sum of $980,000 USD has been calculated.000 USD, while with the study we have carried out we have calculated the sum of $3,856,710.30,being a considerable loss that the insurance does not cover and is assumed by the mining company or mining contractor, this study will be a valuable contribution for the top management of the companies.It is concluded that with the previous research methods it is not possible to know the real cost of the loss with which the company could make a favorable investment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".