Saskatchewan's Place in the Canadian Oil Sands
Bibliographic record
Abstract
Canada's oil sands are well recognized internationally, with Alberta's mined and in-situ oil sands reservoirs being well developed with mature commercial technologies. The next frontier in Canadian petroleum development will be the shallow in-situ oil sands residing in both Saskatchewan and Alberta. Presenting opportunities and challenges that are distinct from the mining and deeper in-situ reservoir situations, the shallow reservoirs will probably need to be developed with new "game-changer" in-situ technologies that will reduce and/or replace the use of steam and fresh water, and dramatically reduce the emissions of greenhouse gases, such as CO2. Significant research and development programs are now aimed at developing and demonstrating such new technologies. Piloting new shallow in-situ development technologies for Saskatchewan's oil sands deposits will enable a new source of Canadian "technology oil" and serve to test more environmentally friendly technologies that could be adapted to current commercial operations. This paper provides a detailed description of the oil sands geology and physical properties as well as highlighting Saskatchewan's oil sands and some of the novel recovery technologies that are being developed for shallow in-situ reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".