On The Wildfire-Induced Changes In The Properties Of A Vegetated Clayey Slope Cover
Bibliographic record
Abstract
Wildfires are generally believed to be detrimental to slope stability, by both damaging the vegetation and altering the hydro-mechanical properties of soil cover through burning action. However, the extent to which wildfires may impact on state of the vegetation and soil state is still an open issue, as it depends on several factors such as fire intensity, on the soil and vegetation state and type. The research activity was carried out with reference to in-situ test-site, the Pisciolo hillslope, where selected vegetation has been seeded and farmed, with the aim to assess its effectiveness in reducing surface water infiltration. The test site caught fire in September 2023, during which most of the vegetation was burned down. Pre and post fire soil properties were evaluated using laboratory and in-situ methods including Loss-on-Ignition (LOI), water drop penetration time (WDPT), and in-situ seepage tests (i.e., by means of Guelph permeameter and double ring infiltrometer). Furthermore, the wildfire-induced thermal stress in the soil was monitored with a thermocouple recording soil temperature within 15 cm b.g.l.. Monitoring results showed that significant wildfire-induced temperature variations were limited to the very near-surface soil layer, up to 25 cm; the soil organic matter decreased after the fire exposure; the hydraulic behaviour was also affected but only to a minor extent, since the coefficient of saturated permeability was found to change only slightly. The logged information may be used for a better understanding of the soil and vegetation post-fire evolution states. Indeed, this research activity is expected to impact the modelling of the slope-vegetation-atmosphere interaction at the ground surface, which is the factor mainly controlling the current activity of several weather-induced landslide in both fine and coarser slopes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".