Differences in Rainfall Interception during the Growing and Non-growing Seasons in a Fraxinus rotundifolia Mill. Plantation Located in a Semiarid Climate
Bibliographic record
Abstract
We estimated the rainfall interception loss (I), canopy storage capacity (S), the ratio of mean evaporation rate from the wet canopy ( E) over the mean rainfall intensity ( R) (mm h-1) ( RE /), and free throughfall coefficient (p) in a Fraxinus rotundifolia Mill. stand located in an afforested Park land in a semiarid region of Iran. For each storm event, I was calculated as the gross rainfall (GR) minus throughfall (TF). S was estimated by indirect methods: the minimum, the mean, and the Gash and Morton. Fifty-five rainfall events were recorded (cumulative GR 197.2 mm), with 31 events occurring during the growing season (total GR 88.0 mm) and 24 events measured during the non-growing season (total GR 109.2 mm). The mean ratio of I to GR equalled 39.2 % during the growing season vs. 23.9 % during the non-growing season. For the growing season, S was estimated to be 0.27, 0.21, and 0.23 mm using the minimum, mean, and Gash and Morton methods, respectively. For the non-growing season, these values were estimated to be 0.17, 0.13, and 0.15 mm, respectively. During the growing and non-growing seasons, RE / were estimated to be 0.13 and 0.11, respectively, with the corresponding p values of 0.39 and 0.52. The loss of the leaves resulted in decline in I, S, and RE / , and increase in p. For semiarid regions, these values are useful for solving some water management problems.
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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".