Spatiotemporal simulation and projection of soil erosion as affected by climate change in Northeast China
Bibliographic record
Abstract
Long-term climate change significantly affects the spatiotemporal dynamics of soil erosion. To explore this, remote sensing technology, future climate scenarios, and deep learning are combined to model the historical and future variations in soil erosion, investigating its spatiotemporal dynamics influenced by climate change. This paper uses the Revised Universal Soil Loss Equation (RUSLE) to assess the historical changes in erosion in northeast China from 1980 to 2020. A soil erosion simulation (SES) model was developed, incorporating deep learning models, to forecast future trends in soil erosion under various climate scenarios. The SES model achieves an R-squared (R2) value of 0.7513. The SES model can simulate the Spatiotemporal dynamics of soil erosion influenced by long-term climate change. Soil erosion from 2001 to 2020 is lower than that from 1980 to 2000, indicating a decrease in soil erosion under natural variability conditions. Unlike historical trends, future soil erosion demonstrates significant variation across three scenarios: SSP1-RCP1.9 (SSP119), SSP2-RCP4.5 (SSP245), and SSP5-RCP8.5 (SSP585). The simulation results show that the SSP119 climate scenario has a minor impact on soil erosion, whereas the SSP245 scenario leads to a gradual increase in soil erosion. The SSP585 scenario, characterized by high social vulnerability and substantial radiative forcing, exacerbates the risk of soil erosion. The study provides valuable references for maintaining soil stability and managing surface runoff.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".