Growing‐season frost is a better predictor of tree growth than mean annual temperature in boreal mixedwood forest plantations
Bibliographic record
Abstract
Increase in frost damage to trees due to earlier spring dehardening could outweigh the expected increase in forest productivity caused by climate warming. We quantified the impact of growing-season frosts on the performance of three spruce species (white, black, and Norway spruce) and various seed sources with different frost tolerance in two plantations, established on both sides of the eastern Canadian boreal-temperate forest ecotone. The objectives of this study were to determine (a) if spruce species and seed sources planted in sites far from their natural provenance would be less adapted to local site conditions, leading to increased frost damage and reduced height growth; (b) at which height above the ground growing-season frosts ceased to damage apical meristems; and (c) if height growth was best predicted by extreme climatic events (growing-season frosts) or by mean annual or summer temperature. At each site and for all spruce species and seed sources, we cross-sectioned spruce trees at different heights above the ground. Tree rings were cross-dated and screened for frost rings, which were then given a severity score based on cellular damage. Frost severity reduced height growth of all spruce species and provenances at both sites. Height growth of the non-native Norway spruce was the most reduced by frost severity and was the smallest species at both sites. Frost caused the highest growth reduction in white spruce at the boreal mixedwood site and had the least effect on black spruce at both sites. For all spruce species, height growth was affected up to 2 m above the ground. Model selection based on corrected Akaike's information criteria (AICc) identified that minimum temperature in May was by far the best climate variable predicting tree growth (AICc weight = 1), highlighting the importance of considering extreme climatic events, which are likely to increase in the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".