Bimodal water retention curves and segmented relative permeabilities of municipal solid waste
Bibliographic record
Abstract
The water retention curve (WRC) and relative permeability are of great importance for performing saturated–unsaturated seepage analysis in soils. The bimodal WRC can better reflect the difference in water retention capacity between macropores and micropores in dual-porosity media compared with the unimodal WRC. Traditional testing methods cannot effectively measure the macropore region in municipal solid waste (MSW) as water is discharged rapidly under gravity. In this study, an implementation framework for determining the bimodal WRCs and segmented relative permeabilities of MSW was proposed, and it was applied on the synthetic sample under sequential levels of overlying stresses. First, a calculation method for dividing the size boundaries of macropores and micropores was proposed from the perspective of energy analysis. Then, the computed tomography scanning combined with the maximal inscribed spheres algorithm was used to obtain the WRC data points for the macropore region, while the traditional pressure plate test was used to obtain the WRC data points for the micropore region. Finally, a modified Van Genuchten model was proposed to fit these data points to yield the bimodal WRCs, and the segmented relative permeabilities were further obtained. In addition, the bimodal probability density curves of pore-size distribution were obtained.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".