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. \n \nThis 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. \n \nPublic 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. \n \nThe 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".