Effects of artificial warming during quiescence on budbreak and growth of white spruce, <i>Picea glauca</i>
Bibliographic record
Abstract
Climate change is expected to increase winter temperatures in boreal climates. White spruce (Picea glauca (Moench) Voss) is vulnerable to spring frost damage due to its habit of early budbreak, which may be exacerbated or lessened with increasingly warm winters at its southern range edge. We tested the effects of episodic warming during the quiescent stage on budbreak time and growth of seven seed sources grown in a common garden setting in Minnesota, USA. Treatment plots were warmed with infrared lamps for 4 days each in February, March, or February and March to simulate a midwinter thaw. Control plots for each treatment and an overall control were included for comparison. Trees warmed in February experienced a slight delay in spring budbreak, but differences in budbreak time were generally not significant. Terminal growth was significantly and negatively correlated with time of budbreak but not with time to growth cessation. Our results suggest that white spruce is relatively resilient to the effects of intermittent warming but that warming early in the season may delay budbreak time, which is expected to reduce terminal growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".