Changes in Day and Night Temperatures and Their Asymmetric Effects on Vegetation Phenology for the Period of 2001–2016 in Northeast China
Bibliographic record
Abstract
Asymmetrical changes in day and night temperatures (Td, Tn) have become a hot topic in climate change research. Using the MODIS 8-day synthetic temperature (MOD11A2) and the SPOT-VGT/PROBA_V 10-day synthetic normalized difference vegetation index (NDVI), we analyzed the spatiotemporal trends of Td and Tn in spring and autumn in Northeast China during the period of 2001–2016 and the partial correlation between temperature and vegetation phenology (start of season, SOS; end of season, EOS). We revealed that spring Td was the main negative factor pertaining to SOS. An increasing Td resulted in a delayed SOS in arid and semiarid regions, while in boreal areas, an increasing Td ensured the heat required for vegetation growth and prompted the SOS to advance. Conversely, EOS was positively correlated with Tn in autumn. Areas showing positive correlations between Tn and EOS totaled 73.26%, and 14.19% of the areas had a significant correlation (p < 0.05). However, the effects of Td on EOS were not as significant as Tn, and only at a 10.62% correlation did Td achieve significance, which was mostly attributable to areas of high elevation and latitude. Our results are crucial for future improvements in dynamic vegetation activities in response to climate change.
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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".