Experimental exposure to winter thaws reveals tipping point in yellow birch bud mortality and phenology in the northern temperate forest of Québec, Canada
Bibliographic record
Abstract
Climate change is expected to increase the frequency and intensity of winter thaws, which could affect leaf phenology. Phenology could either be advanced through the acceleration of forcing accumulation or chilling completion, or be postponed through a reduction in chilling associated with warming air temperature. We tested the influence of winter thaws on budburst phenology by exposing 300 tree cuttings of sugar maple and yellow birch trees to five different frequencies and durations of winter thaws in the lab. In spring, half of the cuttings were exposed to air temperature in two cities representing an air temperature gradient of + 2.0°C to mimic the ongoing climate warming and bud phenology was monitored three times a week. Irrespective of thaw treatment, yellow birch bud phenology occurred earlier in the warmer city, showing the importance of spring temperature in triggering budburst. The treatment with the highest frequency and duration of thawing increased bud mortality and delayed the onset of spring budburst whereas low frequency treatments did not, thereby identifying a tipping point (3 days twice a month) in the impact of winter thaws on bud phenology. Past this point, winter thaws could slow down bud phenology induced by warmer spring temperature and limit carbon uptake by delaying the closure of the canopy. Climate change simulations projected by the CMIP6 Canadian downscaled climate scenario show that winter thaws will increase in frequency Hence the expected advance in spring leaf emergence associated with warmer spring is not necessarily as straightforward as previously thought.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".