Mapping a Nutrient-Rich Groundwater Plume from a Septic System to Lake Water Using Electrical Resistivity Tomography
Bibliographic record
Abstract
Summary Harmful algal blooms resulting from excessive nutrient (phosphorus, nitrogen) loads to inland/marine waters can cause severe environmental and economic consequences. A growing source for nutrient loading to nearshore aquifers, with subsequent migration and discharge to surface water bodies, can be derived from failing septic systems. Understanding the fate and transport of nutrients within the subsurface is needed to implement effective mitigation strategies; however, traditional approaches provide limited information. Previous studies have demonstrated the correlation between nutrient concentrations and electrical conductivity, suggesting that electrical resistivity tomography (ERT) can delineate nutrient contamination. In this study, ERT was used to map the extent of a nutrient-rich groundwater plume migrating from a septic system through a nearshore aquifer and discharging to a lake. The field investigation was conducted at a beach in Ipperwash, Ontario, Canada, which is served by a public restroom. ERT identified a low electrical resistivity plume, commencing beneath the restroom septic system and extending to the shoreline. Groundwater samples collected at multiple depths/locations were analyzed and compared to the ERT-measured bulk resistivity, with a strong correlation existing between resistivity and porewater conductivity, nitrate and phosphorus. This study highlights the value of ERT as a field tool for mapping nutrient-rich groundwater plumes.
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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) | 0.000 | 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".