Artificial Sweeteners Reveal Septic System Effluent in Rural Groundwater
Bibliographic record
Abstract
It has been widely documented that municipal wastewater treatment plant effluents are a major source of artificial sweeteners to surface waters. However, in rural areas, the extent to which septic systems contribute these same compounds to groundwater aquifers is largely unknown. We examined the occurrence of four commonly used artificial sweeteners in an unconfined sand aquifer that serves as a water supply for rural residents, as a receptor of domestic wastewater from septic systems, and as a source of baseflow to the Nottawasaga River, ON, Canada. Groundwater from the Lake Algonquin Sand Aquifer in the southern Nottawasaga River Watershed was collected from private domestic wells and as groundwater seeps discharging along the banks of the Nottawasaga River. Approximately 30% of samples had detectable levels of one or more artificial sweeteners, indicating the presence of water derived from septic system effluent. Using acesulfame concentrations to estimate the fraction of septic effluent in groundwater samples, ∼3.4 to 13.6% of the domestic wells had 1% or more of their well water being derived from septic system effluent. Similarly, 2.0 to 4.7% of the groundwater seeps had a septic effluent contribution of 1% or more. No relationship was found between the concentration of acesulfame and the concentration of nitrate, ammonium, or soluble reactive phosphorus in the groundwater, indicating that septic effluent is not the dominant source of nutrients in the aquifer. It is expected that the occurrence of artificial sweeteners in shallow groundwater is widespread throughout rural areas in Canada.
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 | 0.000 | 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".