Characterizing Vegetation Phenology Shifts on the Loess Plateau over Past Two Decades
Bibliographic record
Abstract
Phenology is a critical mirror reflecting vegetation growth and has a major impact on terrestrial ecosystems. The Loess Plateau (LP) is a paramount ecological zone in China that has experienced considerable vegetation changes. However, understanding the dynamics of vegetation phenology is limited by ambiguous vegetation interpretation and anthropogenic-induced forces. This study combined the multi-climatic and anthropogenic datasets to characterize the interactions between phenology shifts and environmental variables. The principal findings were as follows: (1) Phenological shifts exhibit spatial heterogeneity and an interannually increasing trend in greenness (R2 > 0.6, p < 0.05). Notably, SOS (the start of the growing season) advances while EOS (the end of the growing season) delays in both the southeastern and northwestern regions. (2) SOS and EOS, primarily in the range of 100–150 and 285–320 days, respectively. Phenological changes vary depending on vegetation types. The forest has an early SOS, within 80–112 days, and a delayed EOS, within 288–320 days. The SOS of shrub is mainly within 80–144 days. (3) EOS shows a strong response to the preseason of each climate variable. Precipitation (R = 0.76), soil moisture (R = −0.64), and temperature (R = 0.89) are the governing determinants in shaping vegetation phenology. In addition, agriculture and urbanization play a significant role in shaping the spatial variations of SOS. These findings provide a basis for a systematic understanding of the processes that affect vegetation growth, which is crucial for maintaining the health and sustainability of arid and semiarid ecosystems.
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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 | 0.001 | 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 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".