Drought intensity affects radial growth and recovery of P. schrenkiana at varying elevations in the Western Tianshan Mountains, China
Bibliographic record
Abstract
As climate change intensifies, forests increasingly face the challenges posed by more frequent and severe droughts. However, the impacts of drought intensity on post-drought growth recovery and compensatory growth in trees remain poorly understood. Understanding the mechanisms through which drought influences tree radial growth and accurately assessing how growth responds to different drought intensities is essential for forecasting forest dynamics. In this study, we used correlation analysis to identify the climatic limiting factors for the radial growth of P. schrenkiana Fisch. & C. A. Mey. ( P. schrenkiana ) across three elevations in the Western Tianshan Mountains of China. We assessed the impact of drought intensity on radial growth. By analyzing the growth resistance, recovery, and resilience of P. schrenkiana in relation to drought intensity, we quantified post-drought growth trajectories. Our key findings are as follows: 1) Drought stress is the primary factor limiting the radial growth of P. schrenkiana . 2) Tree growth responses vary significantly with elevation and drought intensity. As drought intensity increased, both resistance and recovery decreased. 3) Compensatory growth occurred following moderate and severe droughts at all elevations. However, this was not observed in the first year after extreme droughts. These findings highlight the importance of the first post-drought year in determining the recovery trajectory of P. schrenkiana radial growth.
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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 | 0.001 | 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".