The Widely Increasing Sensitivity of Vegetation Productivity to Phenology in Northern Middle and High Latitudes
Bibliographic record
Abstract
Abstract Although vegetation phenology generally alters productivity, spatiotemporal variations in this effect and its potential drivers remain unclear. We used satellite‐based vegetation phenology and gross primary productivity (GPP) data sets to analyze trends in the sensitivity of spring GPP to spring phenology (spring S GP ) and autumn GPP to autumn phenology (autumn S GP ). We also explored potential drivers across the northern middle and high latitudes (>30°N) from 2001 to 2019. Our analysis revealed significant increases in spring and autumn S GP ( P < 0.05), with pronounced increases in boreal forests and tundra biomes. In contrast, spring S GP significantly declined in deserts and xeric shrublands ( P < 0.05). Spring temperatures and leaf area index (LAI) were key factors influencing spring S GP , while autumn LAI and downward surface solar radiation drove the variation in autumn S GP . Our findings highlight the critical role of phenology‐productivity interactions in achieving carbon goals and the need for future research on climate feedback mechanisms.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".