Relating aerial erosion, soil erosion and sub‐soil erosion to the evolution of Lunan Stone Forest, China
Bibliographic record
Abstract
Abstract Stone forest (‘Shilin’ in Chinese) is a unique karst landform with a complex evolution process. Based mainly on the characteristics and interrelationships of sub‐soil, soil and sub‐aerial erosion in Lunan karst area, the authors develop a triplex erosion model to describe the evolution of stone forest, and apply it to examine the current development stage and the prospect of the Lunan Stone Forest. The study shows that sub‐soil corrosion, a basic driving force for the vertical scope of a stone forest, usually occurs within 10 m below ground surface but is observed to be most active within the top 2 m, which constitutes the best development zone for stone forest. Under modern climatic conditions, the tip of the stone pillars in Lunan karst area is lowering at a rate of 10·4 mm ka−1, whereas the base of the stone pillars is deepening at 26·17 mm ka−1. Therefore, the height of stone pillars is increasing at a rate of 15·77 mm ka−1. Considering that soil erosion in the study area is as high as 650 mm ka−1, the visible height of the stone forest is actually increasing at a rate of 639·6 mm ka−1. However, the best evolution time for Lunan Stone Forest has already passed despite the fact that it is still growing taller at the present time. This is because the soil layer, which plays an extremely significant role in the heightening of stone pillars, is rapidly thinning at a rate of 623·83 mm ka−1. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".