Simulating seasonal variations of tile drainage discharge in an agricultural catchment
Bibliographic record
Abstract
Abstract Seasonal variations of tile drainage discharge were simulated in the 6 km2 Fensholt catchment, Denmark, with the coupled surface and subsurface HydroGeoSphere model. The catchment subsurface is represented in the model by 3 m of topsoil and clay, underlain by a heterogeneous distribution of sand and clay units. Two subsurface drainage networks were represented as nodal sinks. The spatial distribution of the heterogeneous units was generated stochastically and their hydraulic properties were calibrated to reproduce drainage discharge for one network and verified with drainage discharge for the other network. Simulated discharge was compared to that of another model for which the heterogeneous sand and clay units were replaced by a homogeneous unit, whose hydraulic conductivity was the mean value of the heterogeneous model. With the homogeneous model, drainage dynamics were correctly simulated but drainage discharge was less accurate compared to the heterogeneous model. Simulated discharge was also compared to that of a larger‐scale model created with the MIKE SHE code, built with the same heterogeneous model. HydroGeoSphere and MIKE SHE generated drainage discharge that was significantly different, with better simulated groundwater dynamics data produced by HydroGeoSphere. Nodal sinks in HydroGeoSphere reproduced drain flow peaks more accurately. Calibration against drainage discharge data suggests that drain flow is controlled primarily by geological heterogeneities included in the model and, to a lesser extent, by the nature of the soil units located between the drains and ground surface.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".