Trembling aspen, balsam poplar, and white birch respond differently to experimental warming in winter months
Bibliographic record
Abstract
Climatic warming may increase temperature variability, especially in winter months, leading to increased risk of early loss of cold hardiness and therefore freezing damage. In this study, changes in cold hardiness (measured based on electrolyte leakage), budbreak, and survival were used to indicate the responses of seedlings of 3 boreal broadleaf species [Formula: see text] trembling aspen (Populus tremuloides Michx.), balsam poplar (P. balsamifera L.), and white birch (Betula papyrifera Marsh.) [Formula: see text] to experimental warming. Seedling responses were greater in winter (January) and spring (March) than fall (November), and were greater in trembling aspen and balsam poplar than white birch. Warming for 5 or 10 days at 16 °C day/–2 °C night with a 10-h photoperiod in winter and spring generally reduced cold hardiness. Combined with freezing temperatures in the postwarming ambient environment, this reduction increased seedling mortality and stem dieback and extended time to budbreak. Cold hardiness increased somewhat 10 days after seedlings were returned to the outdoor environment following warming in spring, when ambient temperatures were less damaging. The resistance of white birch to warming, likely because of its greater thermal requirement for budbreak and slower natural dehardening, suggests that this species is better suited to withstand increasing winter temperature variability that might occur under climate change. To improve the accuracy of phenological modelling, the effects of winter freezing on budbreak should be factored in.
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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 | 0.001 |
| 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".