Insights on nitrate transport in a shallow, sandy aquifer at various temporal and spatial scales
Bibliographic record
Abstract
Nitrogen is necessary for successful crop growth, but excess nitrogen in water has implications for both environmental and human health. The factors driving these impacts and their extent remain incompletely understood. In particular, how average nitrogen concentrations compare to concentrations following intense rain events is not well known, partly due to the challenges of building spatially and temporally realistic concentration datasets. Thus, the aim of this study was to investigate hydraulic and nutrient dynamics in a sand plain aquifer system in the Laurentian Great Lakes Basin (north of Lake Erie in Ontario, Canada) through monthly and sub-daily groundwater and surface water sampling to contextualize storm event responses. A study was conducted across the Lower Whitemans Creek (LWC) subcatchment and at a field scale site. Spatial and temporal variations in nitrate concentrations and field parameters were measured in groundwater and surface water monthly from October 2021 to November 2024. Event-based sampling campaigns were conducted using either an ISCO autosampler with a 2-hr interval (in November 2022 and March 2023) or SUNA/EXO monitoring stations with a monitoring interval of 15-60 min (at varying times during October 2022 to November 2024). The results showed that shallow groundwater loaded with NO3-N discharging to small creeks is apparently a notable contributor to elevated levels in Whitemans Creek. It was also observed that the high sampling frequency, carried out via in-situ monitoring equipment, provided marked advantages over automated grab sampling methods. The study highlights the benefits and limitations associated with the different sampling methods to guide future research related to nitrogen quantification, including enhancing the sampling procedures and dataset collection approaches.
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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 | 0.000 |
| 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".