Hybrid Multilevel System for Monitoring Groundwater Flow and Agricultural Impacts in Fractured Sedimentary Bedrock
Bibliographic record
Abstract
Abstract Understanding agricultural contamination in bedrock aquifers is challenging due to complexity of fracture networks and limitations in data acquisition imposed by instrumentation and drilling costs. Engineered assemblages known as multilevel monitoring systems ( MLSs ) maximize data from each borehole by providing numerous, depth‐discrete monitoring intervals for profiles of hydraulic head and hydrochemistry. This article describes a hybrid MLS that uses key components of the Waterloo MLS , with extra piezometers of sufficient diameter to accommodate removable transducers for continuous pressure monitoring, attached to the outside using custom clamps. Monitoring intervals are created with sand packs separated by bentonite seals, either via backfilling from surface or tremie placement. The hybrid MLS is best suited for use in rotary drilled boreholes 12 to 15 cm diameter, smaller holes have insufficient space for the MLS and added piezometer(s) while larger holes have excessive backfill material and purge requirements. Variations were installed to 60 m depth in sandstone in Prince Edward Island, and to 150 m in dolostone in southwestern Ontario. Transducers in the external piezometers provided temporal head data under ambient and stressed conditions in key intervals, and manual measurements in all ports provided detailed vertical hydraulic snapshots. Combined with hydrochemistry profiles from groundwater sampling, the hybrid MLS provided detailed composite datasets for interpreting flow and transport in these bedrock aquifers. The hybrid MLS offers promise as a versatile low‐cost option for groundwater studies in agricultural areas providing improved insights on groundwater flow systems and vertical distribution of nitrate and other contaminants, allowing more informed management decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".