Bibliographic record
Abstract
Abstract Approximately 12 bbl of water are used for the production of each barrel of bitumen in surface-mined oil sands operations. Despite the fact that a significant amount of this water is recycled, surface-mined oil sands typically have approximately 4 bbl of water consumed per barrel of bitumen production. This water is not lost but stored on site and associated with the sand, silt, and clay mineral components left after bitumen is recovered from the oil sands. The silt and clay suspension is called fluid fine tailings and is commonly contained behind large dikes, commonly constructed using the sand component of the tailings or residue from the extraction process. Currently, the lowest cost tailings management and reclamation option is the storage of the fluid fine tailings under a water cap in an end pit lake. The environmental implications of this tailings management strategy are mostly unknown but certainly would require additional water to provide the water cap. Some of the tailings management options that would lead to a dry stackable tailings naturally also significantly decrease the barrels of water associated with each barrel of bitumen production. Currently, somewhere between 800 million and 1 billion m 3(28 billion and 35 billion ft3) of fluid fine tailings stored in various operating company tailings ponds exist, and it could be argued that the pace of reclamation and the implementation of dry stackable tailings technology have been slow because tailings pond areas are continuing to grow. During the last 5 yr, however, a tremendous amount of progress by researchers and industry has been observed in demonstrating dry stackable tailings technologies that will not require fluid tailings storage and therefore allow for reclamation of the original boreal forest. Commercialization of some of the dry stackable tailings technologies will have implications in terms of extraction process water quality and in the ability of industry to meet the recent Energy Resources Conservation Board (ERCB) Directive 74 that mandates how fluid fine tailings will be handled in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".