The Wa-Pa-Su Project Sustainability Rating System: A Simulated Case Study of Implementation and Sustainability Assessment
Bibliographic record
Abstract
Large-scale projects create a variety of social, economic, and environmental impacts throughout their life cycles. Assessing sustainable development becomes a measurable factor, not only for the organizations directly involved in the development, construction, and operation of projects, but also for a number of other stakeholders. In the oil sands and in heavy oil operations, assessment turns into a periodical task, since the construction and operation phases of the projects can last for a considerable period of time. The sustainability assessment tool must have the capability for the organizations and/or projects to evaluate and improve performance over time. The Wa-Pa-Su project sustainability rating system’s design and characteristics meet the sustainability assessment needs of the oil sands and heavy oil operations; therefore, the development of its structure is based to support each area of operation (i.e., sub-divisions) and address the diverse impacts (i.e., areas of excellence) in each pillar of sustainability (i.e., social, economic, and environmental). Though the different sustainable development indicators (SDIs) are incorporated with the aim of measuring the sustainable development of the oil sands projects, the assessment methodology used for measuring sustainability can be implemented in a large range of projects and organizations due to its integrated approach. Since the Wa-Pa-Su project sustainability rating system is the first of its kind focusing on industrial projects with an emphasis in oil sands and heavy oil, it must be understood that a variety of SDIs have not yet been measured, and the data required for this purpose have not been collected; therefore, the objective of this paper is to highlight the flexibility and applicability of the rating system by presenting a simulated case study of implementation and sustainability assessment using an integrated approach.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".