Field evaluation and comparison of two models for simulation of soil‐water dynamics
Bibliographic record
Abstract
Abstract The growing interest in simulation of water and solute movement in soils is in response to the need for development of solutions for various agricultural and environmental management problems. In order to be able to adopt models for simulation of the effects of various soil management practices with confidence, it is important that the capabilities of these models and credibility of their results be tested. In this study, predicted soil‐water contents by the simple LEACHW and comprehensive ecosys models are compared against field measurements using TDR during a selected period with heavy precipitation. A detailed examination of actual soil‐water status, during and after intense precipitation events showed an underestimation of actual drainage fluxes by LEACHW. Such events contribute most in the production of drainage fluxes. Differences in algorithm adopted by the two models are presented and discussed. The algorithm of ecosys resulted in more dynamic water fluxes between layers, which has resulted in better‐predicted results than LEACHW, especially at the soil surface. Overall, performance of the two models was found to be reasonable for prediction of soil‐water. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".