A New Method for Developing Equations Applied to the Water Retention Curve
Bibliographic record
Abstract
The soil water retention curve (WRC) represents the relationship between soil suction and water content. Previous equations for representing the WRC do not include a direct relationship between features of the curve and equation fitting parameters. We present a new method for developing equations for representing empirical data that ensures that features of the curve are directly related to fitting parameters in the equation. The method also ensures that the equation has a simple derivative and is readily integrated, an important property if the equation is to be used as part of a numerical model. This method has been applied to the WRC, producing a new equation that is able to accurately represent the entire WRC using a single equation. The direct relationship between the main features of the curve and fitting parameters ensures that the fitting parameters have clear theoretical meaning. Additionally, this direct relationship allows fitting parameters, such as the air‐entry value, to be calculated using pedotransfer functions. The equation has been fitted to a wide range of experimental data and has been demonstrated to achieve an excellent fit for sand, silt, and clay soils across the entire suction range from the minimum measured suction value to 10 6 kPa.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".