Soil moisture and ecosystem vegetation health effects on drought severity
Bibliographic record
Abstract
INTRODUCTION: Droughts are expected to become more severe due to climate disturbances, posing a serious risk to ecosystems. Therefore, quantifying the drought severity and the resilience of soil moisture and vegetation greening is essential for studying whether the local ecosystem is approaching an alternative state that may be dangerous for agriculture. OBJECTIVE: This study aimed to explore the interactions among vegetation, soil moisture, and drought severity to identify the sensitivity of grid cells to drought under maximum cumulative water deficit critical thresholds and the influence of adaptation factors. METHODS: Drought severity and climate disturbance in a local ecosystem were quantified using dynamically adjusted thresholds, a composite drought index, and a dimensionless index based on water-use efficiency. RESULTS: Moderate and severe drought events were observed using only the drought index. However, these identified events differed across grid cells using the leaf area index representing vegetation health and soil moisture thresholds, suggesting less coverage of drought-affected areas. A substantially reduced drought severity event using adaptation factors showed that local climate and adaptation could significantly change these events. CONCLUSIONS: These findings provide new insights into vegetation greening and soil moisture resilience in various regions under drought conditions. The adaptation factor approach significantly reduced the severity of drought tipping events, indicating that local climate and adaptation may affect drought tipping events.
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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 | 0.001 |
| 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) | 0.001 | 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".