The Canadian oil sands development: Management of land, air and water resources
Bibliographic record
Abstract
The Canadian oil sands -the third largest proven reserves of oil after Saudi Arabia and Venezuelaare located in Northern Alberta, Canada; these deposits of unconventional oil are an attractive investment for oil & gas developers and governments not only because of the size of the reserves but also the geopolitical stability of the region.Nevertheless, the development of the projects and current operations are facing opposition and struggling with a negative campaign around the world due in part to misinformation of the facts, lack of knowledge of the process throughout the projects life cycle, and years of stakeholder mismanagement.Indisputably, the development of the projects carries a series of impacts (environmental, social, economic, and health); developers and operators may dispute the intensity of the impacts but not the existence of them.The manuscript discusses the impacts on land, water and air resources based on indicators included in sustainability reports presented by oil sands developing and operating organizations.These reported statistics give a broader understanding of the current state of the Canadian oil sands and their development.The aim of the manuscript is to present these statistics four most common environmental impacts from oil sands development: greenhouse gas emissions, land use, water use, and tailing ponds.
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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 | 0.001 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".