Onset of autumn senescence in High Arctic plants shows similar patterns in natural and experimental snow depth gradients
Bibliographic record
Abstract
Predicted changes in snow cover and temperature raise uncertainties about how the beginning and the end of the growing season will shift for Arctic plants. Snowmelt timing and temperature are known to affect the timing of bud burst, but their effects on autumn senescence are less clear. To address this, researchers have examined senescence under natural and experimental environmental gradients. However, these approaches address different aspects of plant responses and the extent to which they can be compared is poorly understood. In this study, we show that the effect of snowmelt timing on the timing of autumn senescence in High Arctic plants is the same between a natural and an experimental gradient in three out of four studied species. While the two approaches mostly produce comparable results, they give in combination greater insight into the phenological responses to predicted climate changes. We also showed that a short warming treatment in autumn delayed senescence by 3.5 days in Dryas octopetala L., which is a 10% extension of the growing season end for this species. Warming treatments have commonly been applied to the whole growing season, but here we show that even isolated autumn warming can be sufficient to affect plant senescence.
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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".