Bibliographic record
Abstract
This article explores diverse ways adopted by companies to find ways to make extracting oil from the sands of northern Alberta a little easier. At Petrobank’s Whitesands site, heat from in situ combustion both melts and upgrades the bitumen in the underground deposit. Horizontal production wells carry the oil to the surface. However, even with the new processes in place, copious quantities of energy and water are needed to produce oil from sands. In situ production processes exploit bitumen deposits that are inaccessible through surface mining. The facility at EnCana’s Foster Creek site processes some of the water used to extract bitumen in situ. That recycled water is then boiled and reinjected below the surface. Environmental arguments aside, many observers contend that the only argument against exploiting the Alberta oil sands that might have any success is economic—that it might cost more than alternatives. The paper concludes that barring some unforeseen calamity, oil demand is expected to outstrip the capacity of conventional petroleum production. Even if wringing oil from the Alberta sands is expensive and energy-intensive, it is probably a cost most consumers will be willing to pay for access to the next easiest oil.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.905 | 0.804 |
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".