Hydraulic “Fracking”: Are surface water impacts an ecological concern?
Bibliographic record
Abstract
Use of high-volume hydraulic fracturing (HVHF) in unconventional reservoirs to recover previously inaccessible oil and natural gas is rapidly expanding in North America and elsewhere. Although hydraulic fracturing has been practiced for decades, the advent of more technologically advanced horizontal drilling coupled with improved slickwater chemical formulations has allowed extensive natural gas and oil deposits to be recovered from shale formations. Millions of liters of local groundwaters are utilized to generate extensive fracture networks within these low-permeability reservoirs, allowing extraction of the trapped hydrocarbons. Although the technology is relatively standardized, the geographies and related policies and regulations guiding these operations vary markedly. Some ecosystems are more at risk from these operations than others because of either their sensitivities or the manner in which the HVHF operations are conducted. Generally, the closer geographical proximity of the susceptible ecosystem to a drilling site or a location of related industrial processes, the higher the risk of that ecosystem being impacted by the operation. The associated construction of roads, power grids, pipelines, well pads, and water-extraction systems along with increased truck traffic are common to virtually all HVHF operations. These operations may result in increased erosion and sedimentation, increased risk to aquatic ecosystems from chemical spills or runoff, habitat fragmentation, loss of stream riparian zones, altered biogeochemical cycling, and reduction of available surface and hyporheic water volumes because of withdrawal-induced lowering of local groundwater levels. The potential risks to surface waters from HVHF operations are similar in many ways to those resulting from agriculture, silviculture, mining, and urban development. Indeed, groundwater extraction associated with agriculture is perhaps a larger concern in the long term in some regions. Understanding the ecological impacts of these anthropogenic activities provides useful information for evaluations of potential HVHF hazards. Geographic information system-based modeling combined with strategic site monitoring has provided insights into the relative importance of these and other ecoregion and land-use factors in discerning potential HVHF impacts. Recent findings suggest that proper siting and operational controls along with strategic monitoring can reduce the potential for risks to aquatic ecosystems. Nevertheless, inadequate data exist to predict ecological risk at this time. The authors suggest considering the plausibility of surface water hazards associated with the various HVHF operations in terms of the ecological context and in the context of relevant anthropogenic activities.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".