Case study of an integrated assessment: Shell's North Field Test in Alberta, Canada
Bibliographic record
Abstract
There is growing recognition of the positive role that integrated assessments (IAs) can play in improving decision-making processes for public and private sector projects. Because IAs can help secure both the regulatory and the ‘social’ license to operate, an increasing number of companies, including Royal Dutch Shell, now require their undertaking for major projects. There are, however, limited published case studies to test IA theory and execution, and to provide practical lessons for others. The purpose of this paper is to summarize the undertaking of an IA for a heavy oil pilot project proposed by Shell in northern Alberta and to identify critical success factors. The paper explores key innovations in: (1) the organizational approach to the IA; (2) the scoping and impact evaluation processes; and (3) external communication of results and internal integration of the findings. The paper also provides lessons for industry, regulators, consultants and communities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".