Fractal characteristics of synthetic soil for cut slope revegetation in the Purple soil area of China
Bibliographic record
Abstract
Ai, Y., Chen, Z., Guo, P., Zeng, L., Liu, H., Da, Z. and Li, W. 2012. Fractal characteristics of synthetic soil for cut slope revegetation in the Purple soil area of China. Can. J. Soil Sci. 92: 277–284. Revegetation of the cut slopes formed during railroad construction can reduce potential hazards, improve the quality of the environment, and beautify the landscape along the railways. Artificial backfill soil is required to anchor the plant roots and satisfy the plant's moisture and nutritional demands on the bare rock face. The soil particle size distribution (PSD), fertility and stability of the reconstructed topsoils are affected by the soil type used as a backfill. In this study, a fractal method was used to characterize the PSD. The relationships between the fractal dimension of the PSD and selected soil properties, including soil moisture, bulk density, and organic carbon are discussed. Various soil samples were obtained from four different land-use environments in the purple soil area in southwest, China: a cut slope reconstructed from rock fragments (RF), a cut slope reconstructed with agricultural soil (AS), a naturally developed slope (NS), and a cropland used for growing Vicia faba (CL). Analysis of the soil samples revealed that: (1) from CL to RF, the sand content increased from 59.6 to 83.9%; (2) the fractal dimension of PSD ranged from 2.605 for RF to 2.725 for CL, being the greater the sand content and the lower the fractal dimension; (3) there existed marked linear relationships between the fractal dimensions and selected physicochemical properties of the soils. Therefore, the fractal dimension of PSD can be a useful parameter with which to monitor the structural deterioration and nutrient loss of the synthetic soils used for the revegetation of a cut slope.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".