Impact of leaf retained water on tree transpiration
Bibliographic record
Abstract
Water retained on tree leaves after rainfall, dew, or fog impacts transpiration. To determine the impact of leaf retained water (LRW) on the transpiration of trees, the difference between transpiration rates of different species with and without water sprayed on the leaves was measured. Results show that transpiration was inhibited by LRW. Both the inhibition extent and duration of LRW were higher on broadleaf species than on coniferous species. Under conditions of saturated LRW of the tree crown, mean transpiration inhibition rates of the test species were 0.32 (Quercus mongolica Fisch. ex Ledeb.), 0.25 (Acer mono), 0.37 (Tilia amurensis Rupr.), 0.31 (Fraxinus mandshurica Rupr.), 0.22 (Pinus koraiensis Siebold & Zucc.), 0.22 (Abies nephrolepis (Trautv. ex Maxim.) Maxim.), and 0.23 (Picea asperata Mast.). Mean inhibition rate and inhibition time for broadleaf species were 0.31 and 115 min, which were 40% higher and 27 min longer than those for coniferous species, respectively. The transpiration inhibition rate of LRW increased linearly with the LRW amount, and there was a higher transpiration inhibition rate in broadleaf species than in coniferous species for the same amount of LRW. Mean rising velocity of the inhibition with LRW amount for broadleaf species was 0.53%, whereas it was only 0.15% for the coniferous species.
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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".