Multiple‐scale soil moisture distribution and its implications for ecosystem restoration in an arid river valley, China
Bibliographic record
Abstract
Abstract The spatial distribution of soil moisture and its multiple‐scale correlations to other environmental factors were examined along the Upper Minjiang River valley, China, a landscape subject to severe land degradation of soil and water erosion but also under investigation for potential ecosystem restoration. Results showed that: (1) Soil moisture was highest in the headwaters, and lowest in the arid valley, while moderate values characterized outside the arid valley. The polynomial model of soil moisture distribution on slopes was concave in the lightly disturbed headwaters, convex in the highly damaged arid valley, while convex on south facing slopes and concave on north facing slopes in highly damaged areas in better environmental condition. (2) Soil moisture was correlated with environmental factors at different scales, where elevation and air humidity were only correlated at the sample plot scale, light intensity and wind speed were found to be significant at both slope and site scales and slope and sample plot scales; while slope angle was correlated at all the three scales. From this we conclude that it is possible to improve soil moisture conditions in the arid valley by lowering slope angle and adding low‐cost irrigation systems. (3) The practical threshold of soil moisture for growing meadows, shrubs, and forests were 11ċ800 per cent, 3ċ925 per cent, and 16ċ078 per cent respectively; the arid valley displayed soil‐moisture conditions unfavourable to forest growth. The planned reforestation project is not ecologically reasonable. Reducing human disturbance and revegetating with natural shrubs and meadows may produce more effective results. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.000 | 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".