Scientific Errors and Ambiguities in Prominent Submissions to Canadian Environmental Assessments: A Case Study of the Jackpine Mine Expansion Project
Bibliographic record
Abstract
In Canada, as in many other developed nations, natural resource development projects meeting certain criteria are required to undergo an environmental assessment (EA) process to determine potential human and ecological health impacts. As part of the Canadian EA process, the Canadian Environmental Assessment Agency generally considers submissions by members of the public and experts. While the allowance of external submissions during EA hearings forms an important component of a functional participatory democracy, little attention appears to have been given regarding the quality of such EA submissions. In particular, submissions to EA hearings by prominent individuals and/or groups may be weighted more heavily in the overall decision making framework than those from non-experts. Important questions arise through the allowance and consideration of external submissions to EAs, such as whether inaccuracies in any such submissions may misdirect the EA decision makers to reach erroneous conclusions, and if such inaccuracies do result in sub-optimal EA processes, how the issues should be addressed. In the current work, a representative recent external submission from a prominent public individual and group to the Shell Canada Jackpine Mine Expansion (JPME) Project EA hearings was examined. The case study submission to the JPME EA hearings appears to contain a number of significant scientific errors and/or ambiguities, demonstrating that the EA process in Canada appears to allow potentially flawed submissions from prominent individuals and/or groups, and these problematic submissions may result in unnecessary delays, expenses, or even erroneous decisions. From a public policy perspective, it is desirable that the Canadian EA process be reformed to minimize contributions that may not result in an accurate assessment of the underlying science for the project(s) under consideration.
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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 | 0.056 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.047 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".