Canada’s Oil Sands Innovation Alliance: Delivering Environmental Performance
Bibliographic record
Abstract
Abstract Imperial Oil, an affiliate of ExxonMobil Corporation, along with other 12 major oil companies in Canada formed an alliance of oil sands producers named Canada’s Oil Sands Innovation Alliance (COSIA). COSIA is a collaborative network with a mandate to accelerate the pace of improvement in environmental performance in Canada’s oil sands through capturing, developing and sharing innovative approaches and best practices. The alliance’s vision is to enable responsible and sustainable growth of Canada’s oil sands while delivering accelerated improvement in environmental performance through collaborative action and innovation. COSIA’s 13 member companies represent about 90 per cent of the crude oil production from the Canadian oil sands. Through COSIA, oil sands producers are sharing new technologies and launching new projects in four key environmental areas: land, water, tailings and greenhouse gases emission. COSIA provides the basis for the participating companies to collaborate and share innovation, knowledge, and technologies to minimize oil sands environmental impact in these priority areas. Launched in 2012, COSIA companies have shared about 560 technologies or innovations that cost roughly $900 million to develop. In addition, 185 projects such as commissioning new studies and developing targeted academic research chairs are moving forward under COSIA. COSIA is the overarching collaborative hub within which companies set priorities, drive and share innovation, and accelerate the pace of environmental performance improvements.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".