Evaluation of LEACHMN under dryland conditions. I. Simulation of water and solute transport
Bibliographic record
Abstract
The ability to simulate the dynamics of soil nitrogen under field conditions will aid our understanding of the nitrogen cycle. Our objective was to test the water and solute components of LEACHMN using data obtained from a field lysimeter study conducted on a medium-textured soil in southwestern Saskatchewan, Canada. Our preliminary evaluation of LEACHMN showed that the retentivity and conductivity functions used in this model were not appropriate for our soil as the original model permitted water transmission through the soil profile too rapidly. We, therefore, incorporated the van Genuchten retentivity function into LEACHMN and used the same soil water retention data to generate the van Genuchten parameters. The modified model was able to reproduce changes in water and chloride concentration after minimal calibration. Overall, the value of 0.45 used for the pan coefficient for soil under fallow and 12 mm used for dispersivity produced a realistic estimation of changes in water and chloride within the soil in the 2 yr of the field experiment. The model reproduced soil water redistribution in a fallow system. There was a tendency to under-estimate soil water content during dry periods, mainly as a result of the model’s tendency to over-estimate evaporation. While the centre of mass of chloride was correctly estimated, the model under-estimated the maximum depth of chloride penetration due to a slight tendency to over-estimate evaporation. Based on our statistical and graphical evaluation of LEACHMN, the modified model is adequate for our subsequent nitrate leaching study. Key words: LEACHMN, lysimeter, dryland, water, chloride, nitrate
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".