Development of an extended Schnute model for more physically realistic representations of soil water retention and moisture capacity curves
Bibliographic record
Abstract
Commonly used soil water retention, θ( h), and moisture capacity, C( h), functions implicitly assume that ( i) the θ( h) data curve is sigmoid-shaped with an inflection and ( ii) the C( h) data curve has a value of zero at soil saturation. Desorption measurements on intact soils indicate, however, that the θ( h) data curve is frequently convex-monotonic in shape with no inflection, and C( h) at saturation is often a finite negative value rather than zero. As these model-data mismatches may cause substantial error in simulation or prediction of near-saturated soil hydraulic properties and water flow, a new “Extended Schnute” θ( h)– C( h) function was proposed that can provide θ( h) curve shapes and saturated C( h) values which are consistent with θ( h) and C( h) measurements. The new function and/or its nested Schnute sub-model provided high-quality and physically realistic fits to desorption data collected from intact cores of coarse sand, loamy sand, loam, clay loam, sandy clay loam, clay, and organic clay soils; and it out-performed or equalled the three-parameter van Genuchten θ( h)– C( h) function for every data-set. The new function also provided accurate and physically realistic representations of θ( h) and C( h) data from structured soils containing macropores and strongly graded pore size distributions. It was concluded that the Extended Schnute model is capable of providing accurate and physically realistic representations for a wide range of θ( h) and C( h) data, and it was further recommended that this model be considered over other models when measurements indicate that θ( h) is convex-monotonic in shape and/or C( h) is not zero at soil saturation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".