A Rating System for Sustainability of Industrial Projects with Application in Oil Sands and Heavy Oil Projects: Origins and Fundamentals
Bibliographic record
Abstract
Measures for assessing the environmental impact and long-term sustainability will become an increasingly important requirement in industrial project management. The concept of sustainability influences all aspects of a project, from its earliest phases: development procedures, design of facilities and infrastructure, operation of the industrial facility, and economics. Project management researchers and practitioners are working together to find effective and efficient methods and techniques to minimize the environmental impact that projects carry. Sustainable rating systems are structured decision-making tools in support of measuring environmental performance throughout the project life cycle, not only complying with government & non-government regulations, but also meeting internal and external standards, procedures, processes, and requirements. The majority, if not all, rating systems created to date focus on buildings and residential housing construction. This paper introduces the development of a rating system to measure the environmental performance of oil sands and heavy oil projects, called the WA-PA-SU project sustainability rating system. A brief history of the concept of sustainability is discussed, and correlated with the three integrated areas related to the development of a sustainable rating system for this industrial sector: oil sands and heavy oil projects, regulations, and rating systems. The paper also discusses the tools and techniques applied in the development methodology of a sustainable rating system, lists some of the expected benefits based on previous use of others ratings systems around the world, and finally concludes with an outline of future research.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".