Reduction of raindrop kinetic energy by moss crust on rare earth tailings and its potential impact on soil erosion
Bibliographic record
Abstract
Water erosion of rare earth tailings poses significant ecological risks, while moss crusts are widely distributed across these tailings. However, their role in erosion mitigation remains poorly understood. Using a single-raindrop simulation experiment, this study quantified the kinetic energy reduction capacity of moss crusts at four developmental stages (I: bare soil; II–IV: increasing cover, biomass, and height). Key results include: (1) Moss crusts markedly increased the accumulated kinetic energy required to disrupt the surface ( E moss ). At 1 cm thickness, E moss values for developmental stages II, III, and IV were 0.04, 0.18, and 4.61 J, respectively—stage IV exhibited 363-fold greater resistance than bare soil (stage I). (2) After moss removal, the underlying soil's resistance ( E under ) dropped sharply (averaging 0.01 J across stages), with no differences between developmental stages. Moss crusts reduced raindrop kinetic energy by 65.04% (stage II), 92.79% (stage III), and 99.72% (stage IV). (3) Resistance correlated strongly with slope and soil moisture: air-dried crusts exhibited higher E moss (0.35 J) than wet samples, and steeper slopes (25°–30°) increased E mos s by 10-fold compared to flat terrain. These findings demonstrate that moss crusts act as effective physical barriers against splash erosion, offering a scalable strategy for ecological restoration of degraded rare earth tailings in subtropical regions.
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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) | 0.001 | 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".