Modeling the Soil-Water Retention Characteristic With Pedotransfer Functions for Shallow Seedling Recruitment
Bibliographic record
Abstract
The soil-water retention characteristic (SWRC) is a necessary parameter in seedling recruitment studies. The SWRC was investigated for three 25-mm increments of the shallow seedling recruitment zone to a depth of 75 mm at three hillslope positions on two hillslope aspects across cultivated field topography. Volumetric water content was determined at matric potentials from saturation to −1.5 MPa for the middle soil increment. Three local pedotransfer functions (PTF) were developed using basic soil physical properties and detailed particle size distribution to estimate the parameters of the van Genuchten model for the middle soil increment. The local PTF were compared with Rosetta, HYPRES, and SOILPROP regional PTF. The local PTF generally predicted water retention better than the regional PTF. Rosetta H4 and H5 models predicted water retention as well as one of the local PTF. The SWRC in the upper and lower soil increments were estimated by local PTF using soil properties from the upper and lower increments coupled with the estimated SWRC from the middle increment. Soil properties used to parameterize local PTF varied with soil depth; however, SWRC did not differ with depth. Where direct measurement of soil hydraulic properties is resource limiting, accurate estimation of local SWRC by regional PTF is possible; however, input of partial water retention information was necessary to achieve accuracy. Using local PTF to estimate the SWRC in the upper and lower profile increments of the seedling recruitment zone indicates that a single SWRC is sufficient to describe the profile in this study.
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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 | 0.000 | 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".