Characterization and Analysis of Liquid Waste from Marcellus Shale Gas Development
Bibliographic record
Abstract
Hydraulic fracturing of shale for gas production in Pennsylvania generates large quantities of wastewater, the composition of which has been inadequately characterized. We compiled a unique data set from state-required wastewater generator reports filed in 2009-2011. The resulting data set, comprising 160 samples of flowback, produced water, and drilling wastes, analyzed for 84 different chemicals, is the most comprehensive available to date for Marcellus Shale wastewater. We analyzed the data set using the Kaplan-Meier method to deal with the high prevalence of nondetects for some analytes, and compared wastewater characteristics with permitted effluent limits and ambient monitoring limits and capacity. Major-ion concentrations suggested that most wastewater samples originated from dilution of brines, although some of our samples were more concentrated than any Marcellus brines previously reported. One problematic aspect of this wastewater was the very high concentrations of soluble constituents such as chloride, which are poorly removed by wastewater treatment plants; the vast majority of samples exceeded relevant water quality thresholds, generally by 2-3 orders of magnitude. We also examine the capacity of regional regulatory monitoring to assess and control these risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".