A Rating System for Sustainability of Industrial Projects with Application in Oil Sands and Heavy Oil Projects: Areas of Excellence, Sub-Divisions, and Management Intereactions
Bibliographic record
Abstract
In the structure of the WA-PA-SU project sustainability rating system, three main aspects are considered: areas or categories of excellence, each with a set of criteria; areas or subdivisions of an oil sands or heavy oil project; and management integration. The resources involved in project development, expectations of stakeholders, and potential environmental impact define the ten areas or categories of excellence: project & environmental management excellence (PEME); site & soil resource excellence (SSRE); water resource excellence (WRE); atmosphere & air resource excellence (AARE); natural & artificial lighting excellence (NALE); energy resource excellence (ERE); resources & materials excellence (RME); innovation in design & operations excellence (IDOE); infrastructure & buildings excellence (IBE); and education, research & community excellence (ERCE). The structure of the rating tool considers the complexity and size of oil sands and heavy oil projects, dividing them in nine different areas or sub-divisions: project integration, provisional housing/buildings, permanent housing/buildings, roads, oil transportation & storage, mining process, in-situ process, upgrading & refining, and shutdown & reclamation. The development of the WA-PA-SU project sustainability rating system offers a proactive approach, which aligns with sustainability principles, for oil sands and heavy oil projects throughout their life cycle phases, the project management processes (e.g. initiation, planning, execution, monitoring and control, and close-out), and the life cycle of sub-projects and processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".