Adding value to a mining corporation through project portfolio management: Combined approaches of portfolio theory and multi-attribute utility theory
Bibliographic record
Abstract
A mining corporation has the potential to extract additional value through management strategies, for example, project portfolio management.In mining, project portfolio management can be interpreted in two forms for a large mining corporation listed on the stock market: (1) the company receives project proposals from its mines (e.g., expansion, equipment replacement, social, and new exploration projects); or (2) the project transitions from one stage to another (e.g., licensing to exploration, exploration to development, development to operation, and operation to closure).The problem is determining which project should be supported to maximize utility (e.g., profit maximization, environmental compliance, social acceptance, and/or increasing resources) while minimizing risks.This thesis applies three approaches to solve project selection: (1) Markowitz Theory, (2) Kataoka's Criterion, and (3) the utility additive method.The performance and applicability of these approaches are demonstrated through case studies, and the advantages and disadvantages of each approach are identified.First, I would like to thank my supervisor, Professor Mustafa Kumral, for his support and guidance in my studies these past two years and express my gratitude to the Peruvian Institute of Mining Engineers
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".