Developing a Reclamation Costing Framework for the Athabasca Oil Sands
Bibliographic record
Abstract
The Athabasca oil sands are a significant component of the economy in Alberta. However, they also represent a large environmental risk. At the end of mining operations, companies are expected to begin closure and reclamation activities, which tend to be very costly. The reclamation costs associated with oil sands operations are currently unknown, creating the potential for unprepared operators and leaving the citizens of Alberta and other stakeholders at risk of liabilities. This thesis aims to create a method of determining the reclamation costs that can be expected for each open pit oil sands operation. A framework is proposed that can be utilized to estimate the reclamation costs associated with various operations. The framework attempts to use a logical thought progression that estimators can follow. The intent is to offer guidance from a large, “big picture” perspective down to minute details, which can then be synthesized into a cost estimate. The framework also makes use of key performance indicators for progress tracking. An example of the framework being applied to a tailings pond is included. Public policies and regulations that have been implemented in the past have been misguided in their attempts to improve the overall sustainability in the oil sands. Although the intentions were good, regulations such as Directive 074 created unrealistic targets and timelines that operators were unable to meet. This thesis discusses these issues and suggest possible improvements to future policies. The state of reclamation in the oil sands is also examined. Historical data from the Alberta Government, the Oil Sands Information Portal, and various oil sands operators is collected and analyzed. The technology being developed by Canadian Oil Sands, Syncrude, Suncor, and Canadian Natural Resources is summarized and the resulting reclamation progress is examined.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".