Bibliographic record
Abstract
The purpose of this thesis is to explain financing decisions from the perspective of mining companies. First, all of the major equity and debt financing instruments available to mining companies are presented and analyzed. Each financing method is assessed on a wide range of criteria. Next, these concepts are applied in case studies of four companies: BHP Billiton, Barrick Gold, Teck Resources, and Noront Resources. For each company, the overall choice of financing methods is shown and analyzed. The effect of financing choices on the weighted average cost of capital (WACC) is calculated. The final section deals with case studies of individual mine finance decisions during the past two years: Teck Resources’ $4.2 billion private debt placement to escape from the brink of bankruptcy, Barrick Gold’s $3.9 billion equity financing to eliminate hedges, Copper Mountain Mining’s Joint Venture with Mitsubishi, and Xstrata’s $5.9 billion rights issue in 2009. The financing options used by mining companies can be divided in 16 types, many of which are unique to the mining industry. Public equity is the dominant form of financing, followed by bonds and debentures, while many newer types of financing are growing in importance. Important considerations for selecting the type of financing include: time needed to arrange, typical interest rate, position in capital structure, effect on balance sheet, effect on credit rating and equity dilution. In many cases, the best form of financing is highly contentious. Large companies that are able to obtain good credit ratings are able to achieve a lower cost of capital than their junior counterparts, while gold companies currently enjoy the lowest cost of capital.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".