Evaluation of soil texture data for estimating soil water retention curve
Bibliographic record
Abstract
The soil water retention curve (SWRC) is one of the basic characteristics used in determining soil hydraulic properties, including unsaturated hydraulic conductivity. As its measurement is time consuming and difficult, much effort has been expended to develop indirect methods, such as pedotransfer functions and empirical relationships, to estimate SWRC. In this study, three methods were evaluated based on estimation of retention models parameters and, consequently, the soil water retention curve. For this purpose, soil data collected from three data bases, totaling 72 soil samples with 11 different textures, were used in this study. The statistical parameters such as: MR (mean of residual), RE (relative error), RMSE (root mean square error), AIC (Akaike’s information criterion) and GMER (geometric mean error ratio) showed that the Saxton et al. (1986) method estimates the soil water retention curve better than the other methods.Key words: Pedotransfer function, soil texture, soil water retention curve
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".