Quantifying the effect of non-spatial and spatial forest stand structure on rainfall partitioning in mountain forests, Southern China
Bibliographic record
Abstract
Forest stand structure plays an important role in rainfall interception and is a focal point in forest hydrology. Previous studies mainly looked at the effect of non-spatial attributes of stands while a few studies addressed the influence of spatial features. The aim of this study was to quantify the effect of stand structure on rainfall partitioning using diameter, height, leaf area index (LAI), neighbourhood comparison, mingling index and uniform angle index. The results revealed that the average accumulative throughfall, stemflow and interception loss accounted for 72.8%–83.2%, 0.5%–11.3% and 13.3%–26.2% of total precipitation, respectively, and significant differences existed in rainfall partitioning. The accumulative interception loss was negatively related to uniform angle index (a measure of tree spatial distribution patterns) as stand structure attribute was not available at each rainfall event. The effects of stand structure on throughfall, stemflow and interception loss varies considerably under different rainfall conditions. The LAI was significantly associated with interception loss for heavy rainfalls. The mingling index was negatively related to stemflow; however, significant relationships existed between mingling index, throughfall, and interception loss for light rainfall (drizzle). Significant positive relationships existed between uniform angle index and stemflow, while significant relationships existed for interception loss for light and heavy rainfalls. The results highlight that stand structure in combination with rainfall patterns influence rainfall partitioning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".