Impacts of irrigation on the deciduous period of teak (<i>Tectona grandis</i>) in a monsoonal climate
Bibliographic record
Abstract
Leaf phenology in tropical deciduous forests significantly influences water and carbon exchanges between vegetation and the atmosphere. Thus, a comprehensive understanding of the effects of hydrometeorological variables on the growing period is essential for predicting plant water use following climate change. We investigated whether leaf phenology, bud break, and leaf abscission in mature deciduous teak trees (Tectona grandis L. f.) were induced by the root zone soil moisture content. Using heat pulse velocity and photographic imagery, we compared the deciduous periods (DPs) of two teak trees under natural conditions and two underirrigated conditions. DPs ranged from 47 to 84 days under natural conditions, whereas irrigation shortened the DP by approximately 2 weeks and promoted high levels of water use for 10 months. Thus, the annual water use of irrigated trees far exceeded that of control trees. Under irrigated conditions, leaf abscission and reduced water use were accelerated when the daily mean vapor pressure deficit exceeded 14 hPa following a period of gradual senescence. This study presents preliminary results regarding the impact of irrigation on the teak tree DP. However, uncertainty remains due to insufficient replication; thus, further tests are needed. Nonetheless, our results may predict leaf phenology after short dry periods.
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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".