Early snowmelt accelerates bud break but has mixed effects on leaf area of understory woody plants in a heavily snow-covered deciduous forest in northern Japan
Bibliographic record
Abstract
Climate change induces earlier snowmelt in most regions and extends growing seasons for woody plants. However, there is still limited understanding of how the relative impacts and interactions of light, temperature, and water conditions altered by early snowmelt affect phenological and morphological traits of understory plants. We conducted snow removal experiments in a heavily snow-covered forest. We compared bud break dates and leaf size developments with the effects of snow removal in understory Fagus crenata, Lindera umbellata, and Viburnum furcatum. Snow removal increased temperature and light conditions around buds but decreased the soil moisture during bud break. Removing snow 1 month before ambient snowmelt accelerated bud break but only by 5.9–11.9 days. Bud break in individuals with snow removal required more thawing degree days around buds than under ambient conditions. Leaf areas of V. furcatum in the snow removal were smaller than those in controls. Summarizing changes in light conditions and leaf area growth, the earlier bud break, and leaf growth did not result in greater light capture potential over the spring period in L. umbellata and V. furcatum. Although earlier snowmelt accelerates bud break and leaf expansion in these plants, this may not result in greater carbon accumulation.
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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) | 0.000 | 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".