The role of rock fractures as a water source for trees growing in karst
Bibliographic record
Abstract
Global warming has led to an accelerated dry-wet transition, causing forests to experience more water stress and water use strategy alterations. This could take a great effect on trees in karst region due to tremendous spatial and temporal variability of soil and rock moistures. In this study, we monitored and compared transpiration (sap flow) responses to meteorological variables, soil moisture content and rock moisture content at five sites with a variety of plant-soil-rock compositions in the karst region of southwest China. Results show that the soil-rock composition generally controlled tree growth and transpiration amount, and over 80% transpiration was concentrated in wet growing period. The thin soils can only offer a limited soil moisture and rock moisture dominated transpiration variability and physiological strategies of tree water-use. High and steady rock moisture in appropriate rock fractures enabled tree to exhibit isohydric behavior that can substantially reduce transpiration and seasonal variability. Conversely, low rock moisture made tree tend to anisohydric behavior that increased transpiration in the wet period for resisting drought stress in the dry period. The transition from isohydric to anisohydric behavior for tracking varying environment could reduce tree transpiration response to meteorological variations, such as vapor pressure deficit, and even results in alteration of tree size dominant transpiration. Since tree physiological behavior is extremely sensitive to climate variations and soil-rock compositions, the future acceleration of wet-dry transition is highly possible to increase vulnerability of ecosystems in the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".