Variability in frost occurrence under climate change and consequent risk of damage to trees of western Quebec, Canada
Bibliographic record
Abstract
Climate change affects timings, frequency, and intensity of frost events in northern ecosystems. However, our understanding of the impacts that frost will have on growth and survival of plants is still limited. When projecting the occurrence of frost, the internal variability and the different underlying physical formulations are two major sources of uncertainty of climate models. We use 50 climate simulations produced by a single-initial large climate ensemble and five climate simulations produced by different pairs of global and regional climate models based on the concentration pathway (RCP 8.5) over a latitudinal transect covering the temperate and boreal ecosystems of western Quebec, Canada, during 1955-2099 to provide a first-order estimate of the relative importance of these two sources of uncertainty on the occurrence of frost, i.e. when air temperature is < 0 °C, and their potential damage to trees. The variation in the date of the last spring frost was larger by 21 days (from 46 to 25 days) for the 50 climate simulations compared to the 5 different pairs of climate models. When considering these two sources of uncertainty in an eco-physiological model simulating the timings of budbreak for trees of northern environment, results show that 20% of climate simulations expect that trees will be exposed to frost even in 2090. Thus, frost damage to trees remains likely under global warming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".