Isotopic Characterization of Sulfate in a Shallow Aquifer Impacted by Agricultural Fertilizer
Bibliographic record
Abstract
Abstract The stable isotope ratios of groundwater sulfate ( 34 S/ 32 S, 18 O/ 16 O) are often used as tracers to help determine the origin of groundwater or groundwater contaminants. In agricultural watersheds, little is known about how the increased use of sulfur as a soil amendment to optimize crop production is affecting the isotopic composition of groundwater sulfate, especially in shallow aquifers. We investigated the isotopic composition of synthetic agricultural fertilizers and groundwater sulfate in an area of intensive agricultural activity, in Ontario, Canada. Groundwater samples from an unconfined surficial sand aquifer (Lake Algonquin Sand Aquifer) were analyzed from multi‐level monitoring wells, riverbank seeps, and private domestic wells. Fertilizers used in the area were analyzed for sulfur/sulfate content and stable isotopic composition (δ 18 O and/or δ 34 S). Fertilizers were isotopically distinct from geological sources of groundwater sulfate in the watershed and groundwater sulfate exhibited a wide range of δ 34 S (−6.9 to +20.0‰) and δ 18 O (−5.0 to +13.7‰) values. Quantitative apportionment of sulfate sources based on stable isotope data alone was not possible, largely because two of the potential fertilizer sulfate sources had an isotopic composition on the mixing line between two natural geological sources of sulfate in the aquifer. This study demonstrates that, when sulfate isotope analysis is being used as a tracer or co‐tracer of the origin of groundwater or of contaminants in groundwater, sulfate derived from synthetic fertilizer needs to be considered as a potential source, especially when other parameters such as nitrate independently indicate fertilizer impacts to groundwater quality.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".