Simulated winter warming has negligible effects on germination success of Acadian Forest tree species
Bibliographic record
Abstract
Dormant seeds that require long periods of cold stratification to become germinable may be most sensitive to increases in winter temperatures caused by anthropogenic climate change. In this study, we used outdoor plots with infrared heaters to simulate the effects of projected winter warming (+6 °C) for Canada’s Acadian Forest Region and compared seed germination success of tree species with varying stratification requirements. We evaluated four seedlots each of balsam fir (Abies balsamea (L.) Mill.), red spruce (Picea rubens Sarg.), white pine (Pinus strobus L.), red maple (Acer rubrum L.), sugar maple (Acer saccharum Marshall), and yellow birch (Betula alleghaniensis Britton). Three central findings emerged from this study: (i) none of the tested species were significantly affected by warming; (ii) the random effect of seedlot explained more variation in germination success of deciduous species than it did for conifers; and (iii) balsam fir seedlots exhibited considerable differences in their response to warming, implying intraspecific variation in depth of dormancy. These results suggest seed germination success of the tested tree species may not be impeded by their individual seed characteristics under the magnitude of winter warming projected over the coming century in our study area.
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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".