Dew formation characteristics at annual and daily scale in xerophyte shrub plantations at Southeast margin of Tengger desert, Northern China
Bibliographic record
Abstract
Abstract Being a potential water source in arid and semiarid regions, it is important to quantify the amount of dew. The objective of this study was to determine the annual formation characteristics of dew and the influence of vegetation reconstruction on dew formation in a revegetated desert area. Effects of condensing surface types associated with different plant microhabitats were investigated. We found soil dew mainly depended on air water vapour near soil surface, and primarily formed at 0–3 cm soil layer. Dew formed on 128 days during a 1‐year experiment period could be divided into 2 different periods; the monthly dew yield and the number of dew formation days were highest from May to October, and the cumulative dew yield of July and August accounted for nearly 50% of the annual dew yield. Biological soil crusts facilitated dew formation, with annual dew accumulation of 15.3, 11.9, and 9.6 mm on moss crust, mixed crust, and dune sand, respectively. A longer daily dew production period was found on moss crusts than mixed crusts and dune sand. Plant microhabitats also influenced dew formation characteristics, less dew condense under plant canopies than on dune sand, but the daily dew production period was longer under plant canopies. The findings from this study will provide important and fundamental information to support accurate assessment of the relative importance of dew and rainfall in research on the hydrological cycle of this region, which will be a significant foundation for vegetation protection and ecological restoration in drought environments.
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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 | 0.000 |
| 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".