Identificación y análisis de los criterios de aplicación de la Tasa de Descuento para el cálculo de la provisión del Plan de Cierre de Minas en las grandes empresas mineras del Perú y su impacto en sus Estados Financieros y rentabilidad en el ejercicio 2016
Bibliographic record
Abstract
The use of the discount rate in the calculation of the provision for mine closure has been one of the most important issues in the accounting of any mining company that is operating in Peru. The International Accounting Standard N° 37 – "Provisions, Contingent Liabilities and Contingent Assets" indicates to the mining companies of Peru that they must calculate the present value by the disbursements they will make because of the provision for mine closure, but the problem arises when these companies question what discount rate they should use to bring to the present value. Mining companies such as Buenaventura, Tintaya, Barrick, Yanacocha, Minsur, among others have used different discount rates for calculating the provision for mine closure. For example, many have used an American bond rate, others have used a WACC, Canadian bonds, among other fees. This thesis identifies and analyzes the criteria used by mining companies in Peru to determine the discount rate that will be used to bring to the present value the future disbursements that are presented in the mine closure Plan to Ministry of Energy and Mines. On the other hand, in our research is recommended with the academic and methodological support of professionals responsible for calculating the provision for mine closure and external professionals responsible for revising the provision, on the rate most appropriate discount to be used by mining companies in Peru.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".