Bibliographic record
Abstract
Does carbon capture and sequestration (CCS) make sense in the oil sands?The rapid expansion of oil sands production in northern Alberta is under scrutiny worldwide due to concerns about its environmental, social, and economic impacts.Environmental concerns include climate change impacts from CO 2 emissions along with more local environmental impacts such as dead birds, cancer clusters, and destruction of boreal forests.Within Canada, oil sands have become an important driver of economic growth, so producers and governments are under simultaneous pressure to reduce environmental impacts while maintaining their economic competitiveness (1-5).The environmental footprint of oil sands production is hotly contested; here we aim to clarify divergent claims about CO 2 emissions by exploring how various choices about the scale of analysis (i.e., system boundaries) determine the emissions estimates, the technologies available to reduce emissions, and perspectives and strategies of stakeholders (Table 1).We pay particular attention to carbon capture and storage (CCS), showing how divergent views about its costeffectiveness emerge from divergent choices about the scale of analysis.Debate about the future of oil sands development is so contentious that even the name of the resource is disputed: proponents typically use oil sands while opponents use tar sands.We use oil sands not to express our views on the debate, but because tar is technically incorrect because tars are products of biomass combustion and are chemically distinct from bitumen.The source material is neither oil nor tar but bitumen, but is most generally described as an example of ultraheavy 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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".