Impacts of hydraulic fracturing on water quality: a review of literature, regulatory frameworks and an analysis of information gaps
Bibliographic record
Abstract
A review of available literature and current governance approaches related to the potential impacts of hydraulic fracturing on water quality (including drinking water) was developed. The paper identifies gaps in literature and (or) current governance approaches that should be addressed to guide decision-makers in the development of appropriate regulatory regimes that will enable assessment of the impacts of hydraulic fracturing on water quality. The lack of credible and comprehensive data are shown to have been a major setback to properly investigate and monitor hydraulic fracturing activities and their potential risks on the environment and water quality. A review of current governance approaches demonstrates that some jurisdictions have implemented baseline and post-operation water quality monitoring requirements; however, there are large variations in site-specific monitoring requirements across Canada and the United States. In light of recent information, a targeted approach is suggested based on risk priorities, which can prioritize sample collection and frequency, target contaminants, and the needed duration of the sampling. The steps outlined in this review help to interface with the public concerns associated with water quality, and appropriately ensure that public health is protected through appropriate water safety planning.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".