Leaf size serves as a proxy for xylem vulnerability to cavitation in plantation trees
Bibliographic record
Abstract
Hybrid poplars are an important renewable forest resource known for their high productivity. At the same time, they are highly vulnerable to water stress. Identifying traits that can serve as indicators for growth performance remains an important task, particularly under field conditions. Understanding which trait combinations translate to improved productivity is key in order to satisfy the demand for poplar wood in an uncertain future climate. In this study, we compared hydraulic and leaf traits among five hybrid poplar clones at 10 plantations in central Alberta. We also assessed the variation of these traits between 2- to 3-year-old branches from the lower to mid-crown and current-year long shoots from the mid to upper crown. Our results showed that (1) hybrid poplars differed in key hydraulic parameters between branch type, (2) variation of hydraulic traits among clones was relatively large for some clones and less for others, and (3) strong relationships between measured hydraulic traits, such as vessel diameter, cavitation resistance, xylem-specific and leaf-specific conductivity and leaf area, were observed. Our results suggest that leaf size could serve as an additional screening tool when selecting for drought-tolerant genotypes in forest management and tree improvement programmes.
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) | 0.000 | 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".