The Effect of Intraparticle Porosity and Interparticle Voids on the Hydraulic Properties of Soilless Media
Bibliographic record
Abstract
Core Ideas Green roofs comprise a soilless medium over an atmospheric equilibrated space. Green roofs represent microcosms of capillary fringe and vadose zone hydraulics. Bulk properties come from the interparticle voids and the intraparticle pores. Capillary retention is fundamental for water storage in a green roof system. Design specifications for green roofs should focus on using a well‐graded medium. An essential component of a building‐integrated vegetation system, such as an extensive green roof, is the layer of lightweight planting medium that supports rooting and stores water. Predicting and describing the stormwater management performance of green roofs requires reliable data regarding the water retention properties of the planting medium. Ten materials proposed for use on green roofs, including four mineral components, three biological components, and three commercial blends, were characterized through measurement of their water release curves (WRCs). In combination with the particle size distributions, the resultant data demonstrate that some of the materials contain measurable intraparticle pore networks in addition to the interparticle void spaces described in classical soil hydrology. The WRCs were also used to model the maximum water storage under static equilibrium conditions throughout a 15‐cm profile of each material. In freely draining, unsaturated green roof systems, the role of the intraparticle pores may be limited to increasing microscale roughness of particle surfaces, thereby reducing film flow under drier conditions. The highly organic, biologically derived materials—screened compost, bark fines, and shredded wood—demonstrated hydrophobicity when air dried, but wetting occurred within <30 min on all occasions, which would be within the time frame of many rainstorms. As with natural soils, the saturated hydraulic conductivity was lower in materials with a higher proportion of fines (<106 μm).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".