3D surface–subsurface modeling of a bromide tracer test in a macroporous tile‐drained field: Improvements and limitations
Bibliographic record
Abstract
Abstract The assessment and forecasting of nutrient loss by tile drains in agricultural areas often rely on physically based models that have adequate representations of macropores and tile drains. Macroporosity has been adequately represented in hydrological models using a dual continuum approach. However, its implementation in hydrological and solute transport models is limited to plot‐scale or to one‐ and two‐dimensional models due to the large number of parameters that are rarely available and the long computational times. The purpose of this study is to simulate a tracer test using a 3D coupled surface–subsurface model to improve the representation of the tracer concentration at the drainage discharge. A three‐dimensional HydroGeoSphere model was developed and calibrated to simulate tile drainage discharge, Br mass discharge, and hydraulic heads from a Br tracer test in a densely tile‐drained field. The conductivity of the drain was one of the most important parameters for drain discharge and solute transport simulations. The model accurately simulated drainage discharge and Br transport to tile drains. However, most of the Br peaks and the late‐time Br mass in the drain outflow were underestimated. Our simulation results indicate that explicitly representing tile drains with seepage nodes allows for a physically based, yet computationally efficient representation of Br transport behavior surrounding tile drains at field scale. However, we cannot confirm that the single‐porosity model with immobile zone is suitable for simulating the Br peaks at the drain outlet and the late‐time Br mass. Improvements to the model include the implementation of heterogeneous soil layers and the inclusion of more measured data to reduce uncertainty during calibration.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".