Is canopy interception increased in semiarid tree plantations? Evidence from a field investigation in Tehran, Iran
Bibliographic record
Abstract
From 30 January 2011 to 30 January 2012, we measured the rainfall interception (I) and canopy storage capacity (S) of individual trees of Pinus eldarica and Cupressus arizonica planted in the Chitgar Forest Park near Tehran, Iran. Gross rainfall (GR) in this semiarid region was measured using the mean of 6 plastic rain gauges placed in an open area adjacent to the trees. To measure throughfall (TF), 20 plastic rain gauges were installed beneath the crowns of 5 individual trees of each species. I was calculated as GR minus TF. S was estimated using indirect methods: the minimum, Gash and Morton, mean, and Pereira methods. The cumulative mean values of relative percentage of I (I:GR) for P. eldarica and C. arizonica trees averaged 44.2% and 34.4%, respectively. Significant negative relationships were observed between the percent of I:GR and GR for P. eldarica (R^2 = 0.63) and C. arizonica (R^2 = 0.67) trees. For P. eldarica, S was estimated to be 1.10 mm, 1.00 mm, 1.09 mm, and 1.05 mm using the minimum, Gash and Morton, mean, and Pereira methods, respectively. For C. arizonica, the corresponding values are 0.58 mm, 0.52 mm, 0.56 mm, and 0.55 mm. This study proposes that in this climate dominated by small storms, planting C. arizonica is preferable to planting P. eldarica. However, the differences in the transpiration of these species should be quantified. Our results also indicated that the I value in this semiarid climate was higher than that of a humid climate.
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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".