Evaluating dual porosity of pelletized diatomaceous earth using bimodal soil-water characteristic curve functions
Bibliographic record
Abstract
Soil-water characteristic curve data for specimens containing either ~1 mm or ~2 mm diameter pellets of processed diatomaceous earth are measured using a variety of methods (Tempe cell, pressure plate, filter paper, and chilled-mirror psychrometer). The measured soil-water characteristic curve data are bimodal, reflecting both the microscopic porosity region within the individual pellets, or intrapellet porosity, and the macroscopic porosity region between the pellets, or interpellet porosity. The bimodal distributions are consistent with scanning electron micrographs that show the existence of microscopic pores within each pellet, and the relatively high total porosities (0.725 and 0.764) for the coarse-grained diatomaceous earth specimens. The measured soil-water characteristic curve data are fit with modified forms of the BrooksCorey, van Genuchten, and FredlundXing soil-water characteristic curve functions to account for the bimodal shapes of the measured data. The average microscopic porosities resulting from the curve fits represent 45.0 and 47.9% of the total porosities for the two diatomaceous earth materials. These percentages of microscopic pore space are consistent with the product literature value of approximately 50% for the same materials based on mercury intrusion porosimetry. Thus, the results illustrate the application of bimodal soil-water characteristic curve functions for determining the microscopic and macroscopic portions of the total porosity of dual-porosity media, such as pelletized diatomaceous earth.Key words: bimodal soil-water characteristic curves, diatomaceous earth, dual porosity, macroporosity, microporosity, soil-water characteristic curves (SWCC), soil suction.
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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".