A simplified method to estimate the soil-water characteristic curve
Bibliographic record
Abstract
This paper proposes a simplified method to estimate the soil-water characteristic curve (SWCC) for both coarse- and fine-grained soils using one-point SWCC measurement and basic index properties. Parameters of the Fredlund and Xing SWCC equation were correlated with the basic properties of 60 soils: 30 soils each of coarse- and fine-grained types. Sensitivity analysis revealed that the location of the one-point measurement at matric suctions of 10 and 500 kPa gave the most reliable SWCC using the proposed method for coarse- and fine-grained soils, respectively. The validity of the proposed method was evaluated using a total of 62 soils collated from published literature with 31 soils each of the coarse- and fine-grained types. The proposed method gives a good estimation of the SWCC and uses fewer parameters when compared with existing one-point SWCC estimation methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".