Effects of climatic variability and human activity on runoff in the Loess Plateau of China
Bibliographic record
Abstract
The Loess Plateau in north-central China has a long history of human activities. As a result of climate change, deforestation and sparse vegetative cover, the region suffers from water shortages and severe soil erosion, significantly influencing efforts for sustainable social development. In order to understand the impacts of climatic variability and human activities on runoff and other hydrological factors in this region, the Luoyugou catchment and its paired catchments (Qiaozidong and Qiaozixi) were selected. Statistical analysis indicated precipitation did not vary significantly whereas the annual runoff decreased from 1986 to 2008, with an abrupt change in 1994. Actual evapotranspiration (AET) increased slightly but not significantly. A comparison of runoff in the paired catchments showed land use changes reduced runoff by more than 38% under the same rainfall conditions. Human activities were the strongest contributor to changes in runoff and AET, at 67% and 90% respectively, while the remaining contributor was climate variation. The influence of various human activities on runoff is quite different, and soil-water conservation initiatives have a greater impact on runoff (about 41%). Thus, human activities were the primary reason for the reduction in runoff in the study catchment compared to climate. Greater emphasis should be given to afforestation and soil-water conservation measures.
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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".