Design of Performance Improvement Factors (PIFs) for Sustainable Development Indicators (SDIs) Metrics for Oil Sands Projects with Application to Surface Mining Operations Based on Continual Performance Improvement (CPI)
Bibliographic record
Abstract
The sustainability assessment approach utilized in the development of the WA-PA-SU project sustainability rating system includes three distinctive areas of knowledge: sustainability, continuous performance improvement, and multi-criteria decision-making. Previously, the study of sustainability led to (1) concluding the need for the development of a rating system for industrial projects, with a particular application to oil sands and heavy oil projects; (2) defining the structure of the rating system; and (3) assisting in the pre-selection of sustainable development indicators (SDIs) for surface mining operations. Assessing the sustainability of projects at certain points in time required the application of a methodology selected by the interested groups and/or stakeholders; however, measuring the improvement of projects in sustainable development performance over time (i.e., continuous performance improvement) presents additional challenges. Certain industries (i.e., oil & gas), projects (i.e., oil sands or heavy oil), or specific operations (i.e., surface mining) require a rating system with a particular level of flexibility, offering the opportunity for developers to improve the performance of operations, and for stakeholders to understand the difficulties-and benefits-of implementing SDIs and perform up to levels of truly sustainable development. The present manuscript introduces the performance improvement factor (PIF), which can be determined using three different methodologies: relevance factor or subjective stakeholder valuation, comparative assessment methods, and links to metrics. Additionally, the continuous performance improvement (CPI) indicator measurement is suggested and discussed for a pre-selected set of SDIs for surface mining operations in oil sands projects. Finally, a brief preamble discusses the proposed integrated approach for sustainability assessment and the part it plays in continual performance improvement, offering a foreword to upcoming manuscripts that discuss the other complementary parts of the integrated approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".