The variability of stemflow generation in a natural beech stand (<i>Fagus orientalis</i> Lipsky) in relation to rainfall and tree traits
Bibliographic record
Abstract
Abstract Stemflow (SF) has been recognised as an important process that can exert considerable effects on the hydrology, biogeochemistry, and ecology of wooded ecosystems. The aim of this study was to quantify the relationship between SF (yields and funnelling ratios, FRs) of beech ( Fagus orientalis ) trees and rainfall characteristics, to evaluate the effects of tree traits on SF yield and the magnitudes of FRs in differing rainfall classes. Event‐based measurements were carried out from April 2016 to November 2017 during the leafed‐out periods in a natural uneven‐aged beech stand located in the Hyrcanian forest of Iran. Tree density in the studied plot was 188 trees ha −1 with a basal area of 51 m 2 ha −1 . SF volume was measured in three diameter classes (10–40, 40–70, and >70 cm; n = 3 per class). During the 25 rainfall events SF, SF%, and FR were 3.22 mm, 0.41%, and 1.11 on average, respectively. The linear regression analysis revealed that gross rainfall had the strongest correlation with SF yield and FR ( P value <.01). The linear regression with the trees structural traits indicated that canopy projected area, diameter at breast height (DBH), and mosses cover percentage, respectively, strongly influence SF yield for rainfall <15 to >50 mm. FR significantly decreased with increasing tree height, DBH, and mosses cover percentage (all P values <.05). Smaller trees concentrated more SF than tall and large DBH trees. Pearson correlation analysis indicated tree height, canopy projected area, and MCP were positively and significantly correlated to DBH ( P value <.01; r ≥ .87). Therefore, SF generation in the present study is more associated with DBH. Our findings could assist managers to optimise the management strategies of deciduous forest via promotion of some large DBH trees along with small DBH trees to optimise water inputs via SF in water‐limited forest ecosystems.
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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".