Evaluation of the Root Zone Water Quality Model (RZWQM) for Southern Ontario: Part I. Sensitivity Analysis, Calibration, and Validation
Bibliographic record
Abstract
Abstract This study focuses on the performance of the Root Zone Water Quality Model (RZWQM) for corn production in southern Ontario. The model was used to simulate the amount of subsurface tile drainage, residual soil nitrate-nitrogen (NO3-N), NO3-N in subsurface drainage water, and crop yield. A precalibration sensitivity analysis of the model was conducted for several key parameters using field data collected at the study site. The RZWQM's hydrology component was most sensitive to the Brooks and Corey fitting parameters and saturated hydraulic conductivity (Ks), while the tile drain flow and the water table depth were sensitive to the Brooks and Corey fitting parameters of bubbling pressure (ψbp) and pore-size-distribution index (λ). The fraction of dead-end pores had relatively little effect on tile drain N loss. The crop yield is most affected by N uptake, age, and evapotranspiration rate. RZWQM simulated evapotranspiration was within the range (568 ± 55 mm) of the observed evapotranspiration. The model simulated corn yield very well (-0.1% difference) at the calibration site; however, it underestimated yield (-14.1%) at the validation site. Overall, the RZWQM simulated tile drain flow, NO3-N loss to tile drainage water, and crop yield with reasonable accuracy, but tended to underestimate the amount of soil NO3-N (mean deviation, -0.971). The inability of the model to handle the spatial and temporal variability of the soil may have affected its prediction accuracy. The model also needs improvement in simulating early spring snowmelt hydrology.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".