Effects of Glacial Sediment Type and Land Use on Nitrate Patterns in Groundwater
Bibliographic record
Abstract
Abstract Growing population centers such as those in southern Ontario rely on fractured bedrock aquifers for drinking water. A threat to these aquifers is posed by surficial nonpoint‐source pollution infiltrating with rainwater and moving through the overlying Quaternary glacial deposits. Investigation of local unconsolidated sediments, and the factors affecting contaminant transport through these, is needed to assess risks to the quality of underlying groundwater resources. In this study, sites with a variety of agricultural land management practices and glacial geologic settings were investigated by employing high‐resolution data collection methods. Geologic data from continuous sediment cores were combined with depth‐discrete hydrogeologic and geochemical parameter measurements using high‐resolution multilevel monitoring wells. Within a relatively small geographic area with three distinct glacially derived sediment types, the three sites exhibited greatly disparate vulnerability to nitrate contamination. The geologic setting, including surface topography and architecture and heterogeneity of sediment types at depth, influenced groundwater flow paths and water geochemistry, and subsequently, nitrate distribution. Although management practices influence the quantity of pollutants leaching to groundwater resources, the physical and chemical properties of the subsurface related to the geologic setting ultimately determine the persistence or attenuation of nitrate, and therefore become important to characterize when evaluating best nutrient and waste management practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".