THE STUDY OF LIVING STANDARDS THE VALUATION OF THE ALBERTA OIL SANDS
Bibliographic record
Abstract
The Alberta oil sands reserves represent a very valuable energy resource for Canadians. In 2007, Statistics Canada valued the oil sands at $342.1 billion, or 5 per cent Canada‟s total tangible wealth of $6.9 trillion. Given the oil sands ‟ importance, it is essential to value them appropriately. In this report, we critically review the methods used by Statistics Canada in their valuation of the Alberta oil sands. We find that the official Statistics Canada estimates of the reserves (22.0 billion barrels) of Alberta‟s oil sands are very small compared to those obtained using more appropriate definitions, which results in an underestimation of the true value of the oil sands. Moreover, the failure to take into account the projected growth of the industry significantly magnifies this underestimation. We provide new estimates of the present value of oil sands reserves based on a set of alternative assumptions that are, we argue, more appropriate than those used by Statistic Canada. We find that the use of more reasonable measures of the total oil sands reserves (172.7 billion barrels), extraction rate (a linear increase from 482 million barrels per year in 2007 to 1,350 million barrels in 2015, and constant thereafter) and price ($70 per barrel, 2007 CAD) increases
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".