Responses of vegetation phenology to the asymmetric changes of temperature in daytime and night‐time in the north of 20°N
Bibliographic record
Abstract
Abstract Focusing on the regional differences in the asymmetric effects of daytime and night‐time warming on plant phenology, (a) the start‐of‐season (SOS) and end‐of‐season (EOS) were extracted based on a normalized difference vegetation index (NDVI) dataset for areas north of latitude 20°N during 1982–2015; (b) the partial Pearson correlation coefficient between phenology and temperature was calculated, excluding the effects of precipitation, drought, and evapotranspiration; and (c) the asymmetric effects of daytime and night‐time temperatures on phenology were analysed from the perspectives of the entire study area, different climate regimes, and different altitudes. The results show that phenology showed opposing responses to changes in daytime temperature (Td) and night‐time temperature (Tn) in spring as well as autumn. However, such asymmetric effects were dependent on the climate. In arid/semi‐arid regions, increasing Td induced drought stress by facilitating evaporation from soil, resulting in a delayed SOS and an early EOS, while increasing Tn was beneficial for seed germination and leaf unfolding in the spring. In temperate climate regimes, the responses of phenology to warming in the day and night were more complicated due to different plant species and precipitation. In cold regions, increasing Td led to the advancement of the SOS, while increasing Tn delayed it. In contrast, night‐time warming in autumn significantly delayed the EOS and daytime warming advanced the EOS in cold regions. Warming affected the seasonal dynamics of plants in spring and autumn to a greater degree at higher altitudes than at lower altitudes, which is consistent with the results of previous studies. These results provide valuable insights into the patterns of the asymmetric relationship between the effects of changes in daytime and night‐time temperatures on plant phenology, over a range of regions and altitudes.
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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 | 0.000 |
| 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) | 0.001 | 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".