Analysis of Spatial and Temporal Characteristics and Spatial Flow Process of Soil Conservation Service in Jinghe Basin of China
Bibliographic record
Abstract
The supply and demand of ecosystem services and their mutual feedback are important for the formulation of basin ecological environmental policies. Simulation of the spatial flow of ecosystem services can clarify the division of areas and can support policy development. This paper takes the Jinghe Basin in the Loess Plateau of China as the case-study area to simulate the spatial flow of soil conservation service on different scales. The results showed that (1) soil erosion situations in Jinghe Basin improved overall, with a trend of first aggravating and then recovering between 2005 and 2015; (2) the amount of annual soil conservation in the basin accounted for more than 75% of the potential soil erosion and showed a trend of first increasing and then decreasing; and (3) using digital elevation model (DEM) data and ArcGIS software, the experiment divided the basin into sub-basins (58 in total) and hydrological response units (HRUs) (e.g., 2181 HRUs in sub-basin #1), which were used to quantify the spatial flow direction and the corresponding amount of soil conservation service on the “HRU—river-sub-basin” scale. The divided supply and demand helped quantify the spatial flow pattern of soil conservation services from HRU to the sub-basin.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".