Bibliographic record
Abstract
Summary Canada's oil-sands industry is often perceived as having poor environmental performance. One focus area is the use of water for oilsands production. Bitumen from oil sands is produced by surface mining or by in-situ thermal extraction. Both technologies are water-based. The oil-sands deposits are situated in northern Alberta, where the river basins have 87% of the provincial average annual water supply but only have 13% of the demand. Oil-sands operators have made significant progress in improving freshwater use productivity (intensity), and water use represents a small percentage of natural supply. For in-situ production, the 2010 freshwater use productivity for the industry was 0.43 units of freshwater per unit of bitumen produced. As an example of continuous improvement, the Imperial Oil Cold Lake in-situ oil-sands operation has improved freshwater use productivity by 90% since 1985 through produced-water recycling and the use of deep saline groundwater. The in-situ oil-sands industry will remain a relatively small water user into the future (2030) using an estimated 0.04 to 0.09% of available supply from the three river basins where it is situated. For oil-sands mining, most of the source water comes from the Athabasca River. The average water-use productivity for oil-sands production between 2006 and 2011 was 2.5 units of Athabasca River water per unit of bitumen and synthetic crude oil produced (3.6 for all freshwater sources). In 2011, the oil-sands mining industry used 0.54% of the annual Athabasca River flow and 3% of the lowest 2011–2012 winter weekly flow. For growth forecasts to 2030, it is estimated that the oil-sands mining industry will require 1.4% of the average annual flow of the Athabasca River. Overall, by 2030, it is projected that the entire oil-sands industry will use less than 0.4% of Alberta's average annual water supply to produce 80% of Canada's total oil production.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".