Wa-Pa-Su project sustainability rating system: assessing sustainability in oil sands and heavy oil projects
Bibliographic record
Abstract
A number of environmental and sustainability rating systems have been developed and used around the world.This trend has been most notable in the building industry, where evolution of construction practices and concerns about environmental impact have led to the development of different environmental and sustainability assessment approaches, strategies, models, appraisals, and methodologies.The implementation of green technology and practices has brought economic, social, and environmental benefits with respect to improving sustainable development performance with an accompanying certification process.The framework for developing rating systems for building systems can be extended and applied in other industrial contexts.As global demand for energy continues to rise, unconventional petroleum extraction and production of petroleum substitutes are both becoming more necessary.Development and operation of unconventional oil projects can have considerable social, economic, and environmental impacts.For example, one the largest unconventional oil deposits in the world is the Athabasca oil sands in northern Canada.Government policy makers, industrial developers, and other stakeholders generally work together to develop oil sands projects in an environmentally responsible manner; however, the projects lack of an effective sustainable development measurement tool.The WA-PA-SU project sustainability rating system is a proposed framework for measuring -in a consistent manner -the sustainability of development of unconventional petroleum projects in oil sands and heavy oil.The intent of the rating system is to have a tool that can be used by companies, stakeholders, and policy makers to measure and understand the range of impacts that projects may have over time.This assessment framework includes -but is not limited to -Environmental Impact 115 www.witpress.com,
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".