Temporal-dependent effects of rainfall characteristics on inter-/intra-event stemflow variability in two xerophytic shrubs
Bibliographic record
Abstract
Abstract. Stemflow is important for recharging root-zone soil moisture in arid regions. Previous studies have generally focused on stemflow volume, efficiency and influential factors but have failed to depict temporal stemflow processes and quantify their relationships with rainfall characteristics within events, particularly for xerophytic shrubs. Here, we measured the stemflow volume, intensity, duration and time lags to rain events of two xerophytic shrub species (Caragana korshinskii and Salix psammophila) and rainfall characteristics for 54 events in the Liudaogou catchment of the Loess Plateau, China, during the 2014-2015 rainy seasons. The results indicated that stemflow dynamics were well synchronized to rainfall processes. The stemflows of C. korshinskii and S. psammophila had larger average intensities (4.7 ± 1.5 and 4.8 ± 1.6 mm h−1, respectively) than that of rain at the event scale (4.5 ± 1.0 mm h−1), and the stemflows were even more intense (20.3 ± 10.4 and 16.9 ± 8.8 mm h−1, respectively) than that of rain at 10-min intervals (10.9 ± 2.1 mm h−1). The average stemflow durations of C. korshinskii and S. psammophila (3.8 ± 0.8 and 3.4 ± 0.9 h, respectively) were shorter than the rainfall duration (4.7 ± 0.8 h). Tested by a multiple correspondence analysis and stepwise regression, rainfall amount and duration controlled stemflow volume and duration, respectively, at the event scale by linear relationships (p
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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".