Impact of roots on the hydrogeological properties of silty soil covers
Bibliographic record
Abstract
Mine tailings storage poses significant environmental risks such as the formation of acid mine drainage. Engineered covers offer a solution by controlling water ingress. Their performance is based on specific soil hydrogeological properties (SHPs). They must support vegetation which can impact saturated hydraulic conductivity ( ksat) and water retention curve (WRC). This study assesses the impact of 4-year-old willow root colonization on silty soil covers using flexible wall parameters and water retention tests. The obtained SHPs were compared with root traits. The results suggested that the hydrogeological properties of the studied in situ samples were not significantly affected by roots. The variation of measured and predicted ksat values spanned an order of magnitude, regardless of the root colonization intensity, up to a root length density (RLD) of 2.98 cm/cm3. RLD showed a significant and positive linear relationship with measured ksat values ( R2 = 0.54). However, when root colonization was low (RLD < 1 cm/cm3), RLD was negatively correlated with the nvG ( R2 = 0.44) parameter of the van Genuchten WRC model, while the opposite relationship was observed for samples with RLD > 1 cm/cm3 ( R2 = 0.61). Additionally, RLD and the coarse root (diameter > 1 mm) volume to fine root volume (C/F ratio) influenced WRCs. Over time, coarser roots may have a more pronounced impact on SHPs; further research is needed.
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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 | 0.000 |
| 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) | 0.001 | 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".