A new model for the soil‐water retention curve that solves the problem of residual water contents
Bibliographic record
Abstract
Summary We present a new model for the soil‐water retention curve, θ ( h m ), which, in contrast to earlier models, anchors the curve at zero water content and does away with the unspecified residual water content. The proposed equation covers the complete retention curve, with the pressure head, h m , stretching over approximately seven orders of magnitude. We review the concept of pF from its origin in the papers of Schofield and discuss what Schofield meant by the ‘free energy, F ’. We deal with (historical) criticisms regarding the use of the log scale of the pressure head, which, unfortunately, led to the apparent demise of the pF. We espouse the advantages of using the log scale in a model for which the pF is the independent variable, and we present a method to deal with the problem of the saturated water content on the semi‐log graph being located at a pF of minus infinity. Where a smaller range of the water retention is being considered, the model also gives an excellent fit on a linear scale using the pressure head, h m , itself as the independent variable. We applied the model to pF curves found in the literature for a great variety of soil textures ranging from dune‐sand to river‐basin clay. We found the equation for the model to be capable of fitting the pF curves with remarkable success over the complete range from saturation to oven dryness. However, because interest generally lies in the plant‐available water range (i.e. saturation, θ s , to wilting point, θ wp ), the following relation, which can be plotted on a linear scale, is sufficient for most purposes: , where k 0 , k 1 and n are adjustable fitting parameters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".