A warmer growing season triggers earlier following spring phenology
Bibliographic record
Abstract
Abstract Under global warming, advances in spring phenology due to the rising temperature have been widely reported. However, the physiological mechanisms underlying the warming-induced earlier spring phenology remain poorly understood. Here, using multiple long-term and large-scale phenological datasets between 1951 and 2018, we show that warmer temperatures during the previous growing season between May and September led to earlier spring phenology in the Northern Hemisphere. We also found that warming-induced increases in maximum photosynthetic rate in the previous year advanced spring phenology, with an average of 2.50 days °C -1 . Furthermore, we found a significant decline in the advancing effect of warming during the previous growing season on spring phenology from cold to warm periods over the past decades. Our results suggest that the observed warming-induced earlier spring phenology may be driven by increased photosynthetic carbon assimilation in the previous season, while the slowdown in the advanced spring phenology arise likely from decreased carbon assimilation when warming exceeding the optimal temperatures for photosynthesis. Our study highlights the vital role of photosynthetic carbon assimilation during growing season in spring phenology under global warming.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".