Large-scale identification of riverbank filtration wells using an isotopic and geochemical approach
Bibliographic record
Abstract
This study proposes a simple isotopic framework for the identification of riverbank filtration wells, using time-series of water’s stable isotopes and electrical conductivity at the watershed scale. Riverbank-filtration (RBF) is a widely used managed aquifer recharge method where the infiltration of surface water is induced by pumping groundwater at proximity of a river or lake, and where the quality of the raw water is controlled by the interaction between ground and surface water bodies. As inventories of RBF sites are rare, they limit the development of specific source protection strategies. In the framework of a project funded by the Ministry of the Environment and the Fight against Climate Change of Québec (MELCC, Canada), 40 municipal wells located at less than 500 meters from a surface water body were sampled for 18 months on a monthly to weekly basis. The underlying hypothesis was that a significant contribution of infiltrated surface water to a pumping well would propagate the temporal variations of the tracers observed in surface water. Results highlighted that 25% of the wells pumped a significant contribution of infiltrated surface water: 15% established a continuous connection during the whole hydrological year, while the other 10% revealed a seasonal connection linked to spring floods. All those wells were located less than 120 meters from the surface water and drilled less than 40 meters deep in a granular aquifer. As the number and strength of spring floods may increase in the future, the seasonally connected wells are particularly vulnerable to global change and might prefigure increasing climate forcing on drinking water supply. The remaining 75% were either evidenced as groundwater only (50%) or lacked continuity in the data acquisition (25%). A simple abacus based on the standard deviation of the tracer distribution was then proposed to facilitate the interpretation and make the methods accessible to all. Based on an affordable and easy to perform sampling protocol for water managers, this framework helps in the assessment of specific risks associated to mixed sources of water, and in anticipating variations in quality of the abstracted drinking water.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".