Chloride leaching in two Ontario soils: Measurement and prediction using HYDRUS-1D
Bibliographic record
Abstract
Saso, J. K., Parkin, G. W., Drury, C. F., Lauzon, J. D. and Reynolds, W. D. 2012. Chloride leaching in two Ontario soils: Measurement and prediction using HYDRUS-1D. Can. J. Soil Sci. 92: 285-296. Deterministic numerical modelling can often be used to complement and extend field results, and to provide extra insight into the mechanisms of water and solute movement within the profile of agricultural soils. Chloride leaching and near-surface soil water content in a Guelph loam and a Maryhill loam cropped to corn (Zea mays L.) were measured over a 12-mo period (October 2007 to September 2008) and simulated using the HYDRUS-1D numerical model (version 4.12). Field measurements and prediction indicated that over 70% of the applied chloride (Cl) was lost to deep drainage (below 80 cm depth) during the winter months (November 2007 to April 2008) in both soils. Normalized root mean square error (NRMSE) values for HYDRUS-1D estimates of near-surface (0- to 30-cm depth interval) soil water content over the growing season (April to September, 2008) were 28% for Guelph loam and 42% for Maryhill loam. The NRMSE value for estimated versus measured Cl mass remaining in the soil profile (0-80 cm depth interval) over the winter months was 17% for both soils. It was concluded that the HYDRUS-1D model can provide reasonable predictions of near-surface soil water content and profile leaching losses of tracer solutes. Further work is required, however, to determine if the predictive ability of HYDRUS-1D might be improved by incorporating the effects of freeze-thaw cycles on soil hydraulic properties and solute leaching. Further study is also required to establish the model's ability to simulate the leaching behaviour of reactive solutes, such as nitrate, in agricultural soils.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".