Projected winter warming unlikely to affect germination success of balsam fir regeneration in Atlantic Canada
Bibliographic record
Abstract
Abstract Climate warming has the potential to influence forest composition and species recruitment over the course of the 21st century. Although many of these impacts are expected to occur during the growing season, important life history events, like seed dormancy release, may be affected during the winter. For seeds of balsam fir (Abies balsamea (L.) Mill.) to germinate, they require a lengthy cold stratification period to break seed dormancy, which may not be experienced under warmer winters. Moreover, within Atlantic Canada, balsam fir populations experience very different climates. Dissimilarities among the genetics of these balsam fir populations and adaptations to their local environments may engender variations in population response to winter warming. In this study, we selected three balsam fir seedlots each from four different seed origin zones within Atlantic Canada and subjected them to simulated winter warming in outdoor seed plots that were heated to ≈ 6°C above the ambient temperature from December to April. Contrary to our hypotheses, germination success of the heated balsam fir seeds did not significantly decrease relative to the controls, and there was no interaction between warming and seed origin zone. Seedlots of some seed origin zones exhibited variable responses to warming, suggesting that dormancy levels substantially differ among populations from similar climates. This diversity in phenotype expression within balsam fir populations may improve this species resilience under future climate change.
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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 | 0.001 | 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) | 0.001 | 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".