Bibliographic record
Abstract
Hydraulic fracturing has received abundant media attention in recent years due to a rapid increase in the use of the technique in combination with horizontal drilling technology to produce oil and gas resources from tight reservoirs.Hydraulic fracturing techniques are also used in a variety of other applications that are unrelated to oil and gas production, including tunnel and dam construction, enhanced geothermal energy, carbon sequestration, groundwater remediation, block cave mining, rock burst mitigation, and water well development.Environmental concerns associated with large-scale hydraulic fracturing in oil and gas reservoirs have resulted in political efforts to ban the technique with legislation now in place in certain states in the US and countries around the world.Concerns include soil and groundwater contamination and induced seismicity.A clear understanding of how hydraulic fracturing techniques are used in various applications is important to avoid unintended consequences of any regulations aimed at hydraulic fracturing in the oil and gas industry.The methodology for each application varies widely in terms of scale, pressures applied, additives, and fracture propagation.Mining rock stress measurements, for instance, focus primarily on the breaking strength of rock, and can be conducted with a small-volume highpressure pump that produces only a few liters/minute.The total volume of water injected may be on the order of tens or hundreds of liters.A typical oil and gas well hydraulic fracture treatment, on the other hand, requires millions of litres of injected proprietary fluid and proppant in order to propagate and maintain the fracture effectively into the reservoir.Though both applications are termed "hydraulic fracturing", they differ greatly in terms of potential impacts to the environment.This paper characterizes a range of hydraulic fracturing applications in terms of the objectives, techniques, and potential for environmental concerns associated with the standard
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".