Concentrations of Artificial Sweeteners and Their Ratios with Nutrients in Septic System Wastewater
Bibliographic record
Abstract
Abstract This study reports the first comprehensive data set of characteristic concentrations of four artificial sweeteners: acesulfame (ACE), sucralose (SUC), saccharin (SAC), and cyclamate (CYC), and their ratios with nutrients, for untreated septic system wastewater. Samples were collected from the tanks of 19 different septic systems from across Ontario, Canada; these had a variety of usages, from single‐family cottages to multiple‐dwelling (campground or resort) facilities and had no additional treatment systems. The artificial sweetener concentrations and their relative proportions were highly variable in some cases, both temporally for several individual tanks and from site‐to‐site. Variability tended to be lower for multiple‐dwelling compared to single‐dwelling systems. This variability likely reflects differing use of artificial sweetener‐containing products. The median concentrations for the complete data set of all four artificial sweeteners (in a range of 10 to 60 μg/L) were of a similar order of magnitude, but slightly higher, than has generally been reported for wastewater treatment plant influent (though these vary substantially globally). Both SUC and ACE provided adequate positive linear relationships for dissolved nitrogen and phosphorus in the septic tanks, while a summation of ACE and SUC concentrations also gave a strong correlation. In contrast, CYC and SAC showed poor linear correlation with these nutrients. These reported ranges for artificial sweetener concentrations and ratios with nutrients may be used in future studies to estimate the contributions of nutrients or other wastewater constituents (e.g., pharmaceuticals, bacteria, and viruses) from domestic septic systems to groundwater, including water supply or irrigation wells, and nearby surface water bodies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".