The influence of vegetation types on water yields in the Da Hinggan Mountains of China
Bibliographic record
Abstract
Climate change and excessive water use are endangering water resources in many areas of the world. As a result there is an urgency to increase available water resources and to improve water supply using vegetation management in catchment areas. The objectives of this study were to determine the effects of four vegetation types (Quercus mongolica F.; QM), Larix spp. plantation; LP), Prunus sibirica L.; PS) and grassland; GL) on water yields by monitoring surface runoff, infiltration, canopy interception and evapotranspiration in the Da Hinggan Mountains, a semi-arid area of China. Surface runoff for each vegetation type was triggered by rainfall of at least 6.6 mm, with surface runoff significantly increasing with rainfall events over 15.8 mm. The QM forest had the highest amount of runoff (1.34 mm), followed by LP (1.06 mm), PS (1.01 mm) and GL (0.69 mm), this accounting for only 0.23% – 0.44% of the total water balance. Infiltration to a soil depth of 10 cm occurred with rainfall events with at least 13 mm, but the depth of infiltration rarely exceeded 30 cm during most rainfall events. More than half of the rainfall was taken up by vegetation during the growing season, with an order of LP > QM > PS > GL. Comprehensive analysis indicated that QM was the most appropriate vegetation for water conservation in this water-limited area, and this vegetation cover could effectively provide more water resources in the local 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 | 0.000 |
| 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".