Investigating artificial sweeteners as groundwater tracers of landfill contamination in a complex landscape
Bibliographic record
Abstract
The Arctic and Subarctic are warming at twice the global average rate, leading to rapid changes in hydrologic systems, including a greater risk of contamination from anthropogenic and geogenic sources. The solid waste facility for Yellowknife, Northwest Territories, was unofficially opened in the 1970s and hazardous and nonhazardous waste was deposited on a site with little containment, leading to contamination of the surrounding groundwater. To understand how this leachate is entering the environment through groundwater, an uncontaminated baseline condition must be determined. Establishing baseline conditions in the region is complicated by the presence of geogenic groundwater contamination, nearby historic and current mining activities, and the lack of existing “natural” groundwater data. My MSc thesis research utilizes artificial sweeteners as an innovative tracer of landfill contamination to differentiate between local contamination sources and baseline groundwater conditions. Artificial sweeteners, whose approval in Canada coincides with the opening of the Yellowknife landfill, are an emerging tracer of landfill contamination that provide a unique fingerprint for leachate of different ages. In August 2022, a synoptic sampling of 25 samples of surface water and groundwater in and around the solid waste facility was undertaken. Samples were analyzed for artificial sweeteners, stable isotopes of water, major dissolved ions, and organic contaminants. The resulting data, analyzed with different geochemical and graphical methods, were compared to existing local datasets from nearby mines and historic data. Geospatial analysis was used to assess the leachate pathways. Three off-site leachate migration pathways through surface and groundwater were identified based on elevated sweetener concentrations and distinct ratios between sweetener types. The results show that a region to the northwest of the site appears to be unaffected by leachate and may be a potential location for background monitoring. The research results provide an improved understanding of hydrologic flow paths and benefit long-term monitoring of other sites such as mines, nuclear storage, and abandoned infrastructure in Arctic and Subarctic regions
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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 | 0.002 |
| Science and technology studies | 0.000 | 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".