HYDRUS (2d/3d) simulation of water flow through sandy loam soil under potato cultivation in Southern Manitoba
Bibliographic record
Abstract
The HYDRUS (2D/3D) modeling tool was used to simulate water flow through subsurface-drained sandy loam soil under potato (Solanum tuberosum) cultivation in Southern Manitoba. The model was used to simulate water flow through a 2-D model domain of dimensions, 15 m width × 2.5 m depth. The model was calibrated and validated with field data measured during the growing season of year 2011 at the Hespler Farms, Winkler, Manitoba. Field measurements, including soil water content and watertable depth, for two test plots under subsurface free drainage were used for the calibration and validation. Weather data were also obtained to estimate reference crop evapotranspiration, which was used as input data in the model. Based on the reference crop evapotranspiration, and crop coefficient of the potato crop, the actual crop evapotranspiration was estimated and compared to the simulated actual crop evapotranspiration results. The results showed that the model was able to account for 50% to 78% of the variation in the estimated actual crop evapotranspiration. With respect to water flow through the soil, the observed soil water content and the simulated soil water content were compared using graphical and quantitative analysis. Based on the coefficient of determination (R2), the model accounted for 68% to 89% variation in the observed data. The intercept of the regression line varied from 0.01 to 0.08, and the slope, 0.75 to 0.99. The Nash–Sutcliffe modeling efficiency coefficient (NSE) varied from 0.62-0.89, the Percent bias (PBIAS) values varied from -1.99% to 1.16%. The root mean square error-observations standard deviation ratio (RSR) values varied from 0.33 to 0.61. The values for the evaluation parameters show that the model was able to simulate the water flow through the soil profile reasonably well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".