Quantifying seasonal, depression focused recharge in the context of public supply well vulnerability
Bibliographic record
Abstract
Abstract Depression focused recharge (DFR) may be a hydrologically important process that impacts the vulnerability of public supply wells, specifically related to pathogenic contaminants. The nature of DFR in glacial moraine environments, such as those located in northern latitudes within North America and Europe, is less well established than in other regions such as the Prairie Pothole Region (Northern United States, Western Canada) and the High Plains Aquifer (Central United States). The objectives of this study were to quantify seasonal infiltration flux beneath a topographically‐closed depression within 50 m of a public supply well and to interpret the impact of this DFR process on well vulnerability. Field instruments including groundwater monitoring wells, pressure transducers, soil moisture sensors and temperature sensors were installed in vertical clusters to capture the dynamics of infiltration, drainage and recharge within the depression feature. Continuous weather data were recorded by a meteorological station at the site. Transient infiltration was quantified during two contrasting hydrological events. The first event (~2 days) was an intense rainfall (>50 mm) on a melting snowpack during the fall season when the soils were unfrozen. The second was a longer (35 day) period during the spring freshet when the surficial soils were initially frozen and subject to diurnal freezing and thawing and occasional precipitation events. The water table fluctuation method augmented by Darcy flux contributions, in addition to numerical modelling using the HYDRUS‐1D model, were used to quantify recharge rates beneath the depression. Numerical DFR estimates and analytical results differed by ±8%. Results indicate that recharge rates on the order of the annual regional average can occur beneath localized features in response to extreme events associated with snowmelt and intense rainfall. Such events may represent a microbial threat to groundwater quality if public supply wells are located nearby.
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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 | 0.001 | 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) | 0.001 | 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".