Water repellency of organic growing media related to hysteretic water retention properties
Bibliographic record
Abstract
Summary With respect to soils, most growing media can exhibit hysteresis during drying/wetting cycles, which greatly affects their hydraulic properties. In the case of organic substrates, hydrophobicity during desiccation could be considered as one of the main factors leading to hysteretic behaviour. The purpose of this study was to estimate the influence of changes in wettability on the water retention properties, θ ( ψ ), of peat and pine bark during a drying/wetting cycle. Major differences in the hydraulic behaviour of the two organic materials studied were observed. For peat, hysteresis was found in the water retention curve (21%) and also in the contact angle/water potential relationship, ( α ( ψ ), 20%), whereas in pine bark, this phenomenon was less pronounced in the water retention curve (10%) and even more limited in the α ( ψ ) curve (> 5%). Water retention hysteresis was successfully modelled using a modified van Genuchten‐Durner approach ( VG α model), which took into account the local hydrophobicity of each poral domain of the porous media, regardless of the extent of hysteresis. Incorporating the parameters of the VG α water retention model into a α ( ψ ) equation to characterize overall or average changes in the hydrophobicity of the material during desiccation resulted in values very similar to those of the contact angles calculated with the capillary rise method. These results indicate that water retention properties of these organic substrates are strongly influenced by hydrophobicity.
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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 | 0.000 | 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) | 0.001 | 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".