Bibliographic record
Abstract
The province of Alberta is endowed with the second largest oil reserves in the world behind Saudi Arabia in the form of bitumen in oil sands, with established bitumen reserves of 27.0 10 9 m 3 extracted from initial bitumen in place of 286.6 10 9 m 3 . This huge resource base presents enormous challenges to produce and upgrade bitumen in an environmentally responsible manner, related to potential greenhouse gas emissions. Extracting this bitumen from oil sands and thereafter upgrading it to synthetic crude oil requires extensive processing using either carbon rejection (coking) or hydrogen addition (hydrocracking). The production of hydrogen for hydrocracking necessitates emissions of CO 2 into the atmosphere which contributes in a significant manner to Alberta’s greenhouse gas emissions. Enhance Energy Inc. and NorthWest Upgrading Inc., two privately funded Alberta based companies, are proposing a solution by constructing CO 2 capture and a backbone CO 2 gathering and distribution system from Alberta’s industrial heartland area, which is the site of current and proposed bitumen upgraders, to enhanced oil recovery fields in central and southern Alberta for ultimate sequestration of such CO 2 . This paper presents an overview of the project which is scheduled for operation in late 2013.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".