Use of an Artificial Sweetener to Identify Sources of Groundwater Nitrate Contamination
Bibliographic record
Abstract
The artificial sweetener acesulfame (ACE) is a potentially useful tracer of waste water contamination in groundwater. In this study, ACE concentrations were measured in waste water and impacted groundwater at 12 septic system sites in Ontario, Canada. All samples of septic tank effluent (n = 37) had ACE >6 µg/L, all samples of groundwater from the proximal plume zones (n = 93) had ACE >1 µg/L and, almost all samples from the distal plume zones had ACE >2 µg/L. Mean mass ratios of total inorganic nitrogen/ACE at the 12 sites ranged from 680 to 3500 for the tank and proximal plume samples. At five sites, decreasing ratio values in the distal zones indicated nitrogen attenuation. These ratios were applied to three aquifers in Canada that are nitrate-stressed and an urban stream where septic systems are present nearby to estimate the amount of waste water nitrate contamination. At the three aquifer locations that are agricultural, low ACE values (<0.02-0.15 µg/L) indicated that waste water contributed <15% of the nitrate in most samples. In groundwater discharging to the urban stream, much higher ACE values (0.2-11 µg/L) indicated that waste water was the likely source of >50% of the nitrate in most samples. This study confirms that ACE is a powerful tracer and demonstrates its use as a diagnostic tool for establishing whether waste water is a significant contributor to groundwater contamination or not.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".