Non‐destructive monitoring of nitrate concentration in a laboratory flow experiment using time domain reflectometry (TDR)
Bibliographic record
Abstract
Nitrates, when found in excess in the groundwater, are a health hazard. Nitrates can easily be transported by groundwater movement and may eventually reach aquifers located far away from the contamination location. Recent research has focused on developing innovative techniques to remediate nitrate-contaminated soils. It is important to monitor the nitrate movement in the groundwater to predict the extent of nitrate contamination. Traditionally, nitrate movement is monitored by sampling soil pore water and analysing it in the laboratory. In this study a non-destructive method, using time domain reflectometry (TDR) to monitor nitrate movement in laboratory flow experiments, is presented. Three flow cells (replicates) packed with a silty loam soil were subjected to a constant hydraulic gradient inducing saturated water flow through the flow cells. A source of potassium nitrate solution, containing 500 ppm NO3-N, was connected at the inflow end, and the nitrate concentration change with time was monitored along the length of the flow cells by sampling the soil solution and analysing it in the laboratory for NO3-N concentration. At the time of sampling, TDR wave forms were recorded using the TDR mini-probes inserted at regular intervals along the length of the flow cells. The bulk soil electrical conductivity and the water content values extracted from the TDR wave forms were used to predict the nitrate concentrations at different locations. The nitrate concentration values predicted from the TDR-measured bulk electrical conductivity and water content data correlated well with the nitrate concentrations obtained by soil solution sampling method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".