Effects of vegetation on hydrological response of silty volcanic covers
Bibliographic record
Abstract
This work examines the hydrological behaviour of a silty volcanic layer exposed to the atmosphere for 3 years under vegetated conditions. The layer was extensively monitored to measure energy fluxes, water fluxes, and internal variables (suction, water content, water storage, and temperature). Measurements were used to build representations of the layer’s behaviour patterns depending on its surface covering, comparing the behaviour in vegetated conditions with behaviour observed under bare conditions over the previous 4 years. Results show that during cold–dry periods, differences in terms of fluxes and hydrological variables between the bare and vegetated conditions reduce to negligible levels, but increase significantly during hot–dry and transition periods. As the soil forming the layer was selected to have the same intrinsic and state properties as the layer in a specific rainfall-induced landslide case history that occurred in Nocera Inferiore (South Italy) in 2005, the experimental results are used to re-interpret such a landslide, considering the effects of vegetation and referring to a coupled thermohydraulic model. The experimental results are used to calibrate the model, and this is then used to interpret around 10 years of meteorological variables recorded at the landslide site, including the landslide time. Comparison with interpretations made previously as a bare soil hypothesis shows how neglecting the effects of vegetation might imply a loss in prediction accuracy of soil state variables (suction and water storage) related to the slope stability.
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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) | 0.000 | 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 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".