Ensuring health and environmental protection in hydraulic fracturing: A focus on British Columbia and Alberta, Canada
Bibliographic record
Abstract
Unconventional natural gas resources recovered using hydraulic fracturing (HF) is contributing to national energy self-sufficiency and could be a significant factor in the global transition to a low carbon economy. Using an integrated risk management framework, we conduct a comparative analysis of practices and review recommendations of a regulatory, economic, advisory, community-based, or technological nature for British Columbia and Alberta, Canada. Lessons learned from international assessments of risk issues are also considered. Overall, there is much less emphasis on potential impacts on human health than on the environment. The analysis also identifies a need for a strong and adequately resourced regulatory framework that works in concert with enhanced technological requirements; evidence-based emissions standards; regulated and/or community-based setbacks and buffer zones; operational surveillance, reporting, and disclosure of value-chain activities in an accessible and transparent way; community participation in the development of these mechanisms; and provision for legacy sites. Economic options such as performance-based taxes and fees, industry-funded studies, the role of carbon taxes, and cost allocations to protect or improve determinants of health are the least advanced option. This analysis provides support for the development of a risk management policy agenda with respect to broad and persistent HF risk management issues.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".