Evaluation of the impact of the Gully Land Consolidation Project on runoff under extreme rainfall
Bibliographic record
Abstract
Abstract Extreme rainfall is an important driver of soil erosion and land damage. The Gully Land Consolidation program (GLCP) was first launched in 2011 as a major land reclamation practice to increase farmland in the Loess Plateau of China. Studying the impact of artificial projects on hydrology can help humans to respond to the various water issues, but the assessment of the effects of the GLCP on extreme rainfall‐induced water runoff at watershed scale is currently lacking. Our study used the soil and water assessment tool (SWAT) to evaluate the influence of the GLCP at different locations and areas on water runoff under extreme rainfall events in the Yanhe watershed. Results showed that: (1) the GLCP can improve the interception of surface runoff, with interception efficiency in downstream of the watershed approximately twice that at midstream and entire watershed as well as seven‐times that at the upstream; (2) when GLCP measures are evenly distributed in a watershed, as the area of GLCP increases from 76.40 km2 (1% of watershed area) to 382.01 km2 (5%), the interception of surface runoff increases by 0.77 mm; (3) and the GLCP can increase soil infiltration and groundwater recharge. This research is expected to provide insights into the optimized layout of the GLCP at watershed scale. Correspondingly, policymakers can refer to this information in developing policies on the sustainable use of land.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".