RUNOFF AND SEDIMENT RESPONSES TO CONSERVATION PRACTICES: LOESS PLATEAU OF CHINA<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT: Soil erosion is the most significant threat to land productivity and environmental quality on the Loess Plateau of China. The annual total sediment load of the Yellow River is 1.6 billion tons, with about 90 percent coming from soil erosion from the Loess Plateau. To reduce soil erosion from the Loess Plateau, conservation practices, including tree planting, ridge construction between fields and around gullies, terrace and ditch construction perpendicular to the main slope, and dam construction are being implemented. An evaluation of these conservation practices is required before they are implemented at the large scale. The objective of this study is to evaluate the effectiveness of conservation practices to control runoff and sediment yield from paired watersheds in the hilly gully region of the Loess Plateau. The advantage of the paired watershed approach is its sensibility in detecting differences in runoff and sediment transport by monitoring both watersheds during two periods, an initial period with no conservation practices and a treatment period with only one watershed subjected to conservation practices. Implementation of the conservation practices resulted in (1) cumulative runoff and sediment yield that were, respectively, 25 and 38 percent less from the treatment watershed than from the control, (2) a decrease in the number of rainfall events producing runoff and sediment transport (94 in the control versus 63 in treatment), and (3) a reduction in the maximum discharge and maximum suspended sediment concentration.
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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 | 0.000 | 0.001 |
| 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".