Surface water and groundwater analysis using aryl hydrocarbon and endocrine receptor biological assays and liquid chromatography-high resolution mass spectrometry in Susquehanna County, PA
Bibliographic record
Abstract
The contamination of surface water and ground water by human activities, such as fossil fuel extraction and agriculture, can be difficult to assess due to incomplete knowledge of the chemicals and chemistry involved. This is particularly true for the potential contamination of drinking water by nearby extraction of oil and/or gas from wells completed by hydraulic fracturing. A case that has attracted considerable attention is unconventional natural gas extraction in Susquehanna County, Pennsylvania, particularly around Dimock, Pennsylvania. We analyzed surface water and groundwater samples collected throughout Susquehanna County with complementary biological assays and high-resolution mass spectrometry. We found that Ah receptor activity was associated with proximity to impaired gas wells. We also identified certain chemicals, including disclosed hydraulic fracturing fluid additives, in samples that were either in close proximity to impaired gas wells or that exhibited a biological effect. In addition to correlations with drilling activity, the biological assays and high-resolution mass spectrometry detected substances that arose from other anthropogenic sources. Our complementary approach provides a more comprehensive picture of water quality by considering both biological effects and a broad screening for chemical contaminants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".