Contrasting Differences in Responses of Streamflow Regimes between Reforestation and Fruit Tree Planting in a Subtropical Watershed of China
Bibliographic record
Abstract
Fruit tree planting is a common practice for alleviating poverty and restoring degraded environment in developing countries. Yet, its environmental effects are rarely assessed. The Jiujushui watershed (261.4 km2), located in the subtropical Jiangxi Province of China, was selected to assess responses of several flow regime components on both reforestation and fruit tree planting. Three periods of forest changes, including a reference (1961 to 1985), reforestation (1986 to 2000) and fruit tree planting (2001 to 2016) were identified for assessment. Results suggest that the reforestation significantly decreased the average magnitude of high flow by 8.78%, and shortened high flow duration by 2.2 days compared with the reference. In contrast, fruit tree planting significantly increased the average magnitude of high flow by 27.43%. For low flows, reforestation significantly increased the average magnitude by 46.38%, and shortened low flow duration by 8.8 days, while the fruit tree planting had no significant impact on any flow regime components of low flows. We conclude that reforestation had positive impacts on high and low flows, while to our surprise, fruit tree planting had negative effects on high flows, suggesting that large areas of fruit tree planting may potentially become an important driver for some negative hydrological effects in our study area.
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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) | 0.000 | 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".