Effects of vegetation cover on sediment particle size distribution and transport processes in natural rainfall conditions on post-fire hillslope plots in South Korea
Bibliographic record
Abstract
Sediments were collected from four slow vegetation recovery plots, six fast vegetation recovery plots and five unburned plots at a post-fire site on a rainfall event basis and sorted for size distribution. The aim was to evaluate the effects of vegetation cover, soil aggregate stability, slope and rainfall intensity on sediment size distribution, transport selectivity and erosion processes between the burned and unburned treatment plots. Sediment detachment and transport mechanisms and the particle size transport selectivity of the eroded sediment were assessed based on enrichment ratios (ER) and mean weighted diameter (MWD) methods. The most eroded particle size class in all treatment plots was the 125–250 µm class and, generally, the percentage of eroded particle sizes did not increase with slope and rainfall intensity. Higher MWD of the eroded sediment was related to a higher percentage of bare soil exposed and gravel content associated with high soil burn severity and soil disaggregation in the slow vegetation recovery plots. The enrichment of finer clay silt particle sizes increased with varying maximum 30-min rainfall intensity (I30) in the slow vegetation recovery plots, and reflected increased aggregate breakdown and transport selectivity, whereas no good relationship was found in the fast vegetation recovery and unburned plots with varying I30. A minimum I30 of <3.56 mm h–1 and a maximum of 10.9 mm h–1 were found to be the threshold rainfall intensity values necessary for aggregate breakdown and transport of finer particles by both rainsplash and rainflow in the slow vegetation recovery plots, whereas the response was weak in the fast vegetation recovery and unburned plots following varying I30 dominated only by rainsplash transport closer to the plot sediment collector. The results show that higher vegetation cover in the fast vegetation recovery and unburned plots reduces erosive rainfall energy by 5.6- and 17.7-fold respectively, and runoff energy by 6.3- and 21.3-fold respectively, limiting aggregate breakdown and transport selectivity of finer particles compared with the slow vegetation recovery plots.
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 | 0.001 | 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".