Tree-based estimation of canopy water storage capacity and direct throughfall coefficient of Cupressus arizonica Green.
Bibliographic record
Abstract
The aim of this study was to estimate the individual tree-based 1) canopy water storage capacity (S), 2) ratio of mean evaporation rate from the wet canopy to the mean rainfall intensity () by the Pereira method, and 3) direct throughfall coefficient (p) for Cupressus arizonica trees. The trees wereafforested in the Chitgar forest park in Tehran which is classified as a semiarid region. Measurements were carried out from February 2011 to February 2012. To measure the gross rainfall (GR), six rain-gauges were installed in an open space adjacent to the trees. Throughfall (TF) was measured using a number of twenty rain-gauges located under the crown of five individual trees. Rainfall interception (I) was calculated as the difference between GR and TF. During the measurement period, 55 rainfall events were recorded with a cumulative depth of 234 mm. The C. arizonica trees intercepted 21.8% and 32.1% of the incident rainfall on cumulative-based and event-based (each GR) manner, respectively. Positive and negative power correlations were observed between I and GR (r = 0.89)as well as between(I: GR) % and GR (r = 0.69) for the mean value of five individual trees. Mean values of S,, and p were estimated as 0.38 mm, 0.14, and 0.46, respectively. I and its elements (), S, and p as well as transpiration of trees are, therefore concluded as necessary parameters to be considered when selecting suitable species for afforestation projects in the arid and semiarid zone. In addition and as a key parameter for calculating I, S can be optimally estimated by Pereira method which is exclusively proposed for tree-based measurements.
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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 | 0.002 | 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".