Investigation of plant growth and transpiration-induced matric suction under mixed grass–tree conditions
Bibliographic record
Abstract
Although evapotranspiration-induced matric suction for single species has been widely studied, little is known about how mixed-species planting would affect plant growth and induced matric suction. This study aims to explore the effects of grass–tree interaction on their growth and induced matric suction during evapotranspiration (ET) and rainfalls. Field monitoring was carried out to measure matric suction responses in compacted soil that was vegetated with (i) single tree species, Schefflera heptaphylla, and (ii) mixed species of the trees and a grass species, Cynodon dactylon. In each condition, three tree spacings (120, 180, and 240 mm) were planted. When tree spacing increased from 120 to 240 mm, the peak tree root area index (RAI, for fine roots with diameter <2 mm) decreased by 16%, but the peak grass RAI increased by 29%. At mixed planting plots, the ET-induced peak matric suction for a tree spacing of 240 mm was 20% higher than that for a spacing of 180 mm because of increased contribution of grass-root water uptake as the trees were more widely spaced. Without grass, a reverse trend was observed as tree–tree interaction reduced at wide spacings. The peak ET-induced matric suction had a significant linear correlation with RAI. During rainfalls, the highest matric suction was preserved for the case of 240 mm spacing due to the greatest reduction of soil hydraulic conductivity by the presence of roots.
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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 | 0.000 | 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 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".