Estimation of bark water storage capacity of broad- and needle-leaved trees planted in a semi-arid climate zone
Bibliographic record
Abstract
In forest ecosystems, measurement of bark water storage capacity (BWSC) is required for determining the amount of rainfall interception and understanding throughfall and stemflow processes. We compared the bark roughness coefficient (BRC) and BWSC of even-aged, widely-planted, broad-leaved and needle-leaved species in the Chitgar Forest Park, near Tehran, Iran. Bark samples (n = 10 per species) were extracted from three needle-leaved species ( Cupressus arizonica , Thuja orientalis , and Pinus eldarica ) and two broad-leaved species ( Acer velutinum and Robinia pseudoacacia ). The highest and lowest BWSC was found to be for A. velutinum and P. eldarica trees, respectively (0.63 vs . 0.32 g cm −3 ). Among the studied species, R. pseudoacacia had the highest BRC (0.69), and the lowest was found to be for A. velutinum species (0.03). The whole tree bole BWSC ranged from 156.8 L for P. eldarica to 14.6 L for A. velutinum . The results suggested that A. velutinum may provide more stemflow to the forest floor due to its small size, low BRC and subsequent low whole tree bole BWSC. These findings significantly contribute to our understanding of rainfall partitioning dynamics and ecohydrological processes associated with stemflow from trees. This improved understanding will help managers in selecting tree hydrologic characteristics that align with their objectives in forestry treatments, afforestation plans and establishment of vegetation in urban park settings within drylands.
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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 | 0.001 |
| 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".