Genetic variation in growth and leaf traits associated with local adaptation to climate in yellow birch (<i>Betula alleghaniensis</i> Britton)
Bibliographic record
Abstract
Understanding patterns of variation in functional traits of hardwood trees is crucial for conserving and managing North American temperate forests under climate change. This study examined provenance variation of yellow birch ( Betula alleghaniensis Britton) in growth, biomass allocation, leaf morphology, and stable carbon isotope composition. Trees were grown from 10 seed sources originating from across Canada and the northern USA. Height and diameter were not significantly related to climate at seed origin, suggesting that variation may be better explained by site factors, such as soil pH and soil moisture. In contrast, carbon isotope composition and leaf morphological traits were significantly correlated to climate variables including temperature, precipitation, and solar radiation. Provenances from warmer, drier localities tended to have higher stable carbon isotope ratio (δ13C), greater specific leaf area, and narrower leaf width than their counterparts from cooler, wetter climates. Thus, variation in leaf morphological traits appears to be involved in adaptation of yellow birch to variation in temperature and moisture availability across the species’ range. Our results suggest that there may exist potential for selection and breeding of drought-resistant yellow birch genotypes to aid in reforestation under climate change.
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 | 0.001 | 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) | 0.001 | 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".