Effect of the particle-size distribution variability on the SWCC predictions of coarse-grained materials
Bibliographic record
Abstract
The particle-size distribution (PSD) is the key information required by several models for prediction of the soil-water characteristic curve (SWCC). The performance of these models has been extensively investigated in the literature; however, limited studies have been undertaken with respect to the uncertainty associated with the SWCC predictions resulting from the variability in the PSD. This study aims to investigate the influence of the variability of the PSD in the prediction of SWCCs using five different models applied to three different glass beads (GBs). The PSD curves were determined by sieve analysis, laser diffraction, and image analysis. The various testing procedures were statistically evaluated to understand the influence of variability of the PSD in terms of the coefficient of uniformity (CU) and de size of particles corresponding to 10% in the PSD (D10). For each prediction model, a combination of PSD curves and their coefficient of variation were used to estimate the SWCCs. Both theCUandD10proved to have a strong relationship with the predicted SWCCs. TheCUappears to influence more the residual suction prediction while theD10seems to have a major role for the transition and residual stages.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".