A Stepwise Multifactor Regression Analysis of the Interactive Effects of Multiple Climate Factors on the Response of Vegetation Recovery to Drought
Bibliographic record
Abstract
In this study, a stepwise multifactor vegetation regression analysis (SMVRA) approach was proposed to investigate the interaction of multiple climate factors on vegetative growth in the study area from 2000 to 2020. It was developed by integrating the stepwise linear regression method, Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Pearson correlation coefficient. SMVRA can be used to intuitively understand the interactive effects of multiple correlated factors (e.g., temperature, precipitation, potential evapotranspiration, and the drought index) upon vegetation. The results show that the resilience of vegetation in the BLR basin is influenced by the severity of drought. Annual changes in SPEI over the BLR basin show an increasing trend, with rates of 3.12 × 10−2. Precipitation and NDVI had a strong positive correlation (p < 0.05), found for 34.93% of the total pixels in the study area. In the BLR basin, vegetation growth is inhibited in the 4 years following a drought event. The area near 800 m is most sensitive to drought events. It provides a theoretical basis for future drought response and effective vegetation restoration in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".