Analysis of Selected Aromatic Hydrocarbons from Drinking Water and Natural Water Potentially Affected by Shale Gas Production
Bibliographic record
Abstract
Summary In Canada, shale gas exploration is underway in several provinces, with most of the production currently on-going in the western provinces. Public concern is growing with regard to potential impacts on the quality and quantity of water resources, most importantly drinking water. Water impact assessment requires extensive collection of baseline data sets. Re-use of produced water for hydraulic fracturing is being encouraged and knowledge of organic impurities present is important in optimising treatment processes. Developing analytical methods for chemical indicators of shale gas exploitation impact on water sources is key for future monitoring studies. An automated SPME-GC-MS method for the quantitation of 22 aromatic hydrocarbons (BTEX, trimethylbenzenes, naphthalene, methyl and dimethylnaphthalenes) with adequate sensitivity for drinking water impact studies is described here. Drinking water samples were quenched with 0.114 M ascorbic acid, extracted using a CombiPAL autosampler equipped with a PDMS fibre and analysed using a GC-MS/MS instrument. The method was tested on untreated and treated water samples. Method sensitivity was adequate for drinking water quality testing. All analytes were stable for 14 days in all tested samples. Analyte recovery ranged from 78% for benzene to 116 % for 1,2,3-trimethylbenzene and was independent of the characteristics of the water.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".