Interactions between xylem traits linked to hydraulics during xylem development optimize growth performance in conifer seedlings
Bibliographic record
Abstract
Summary Climate change has threatened forests globally, challenging tree species’ ability to track the rapidly changing environment (e.g., drought and temperature rise). Conifer species face strong environmental filters due to climatic seasonality. Investigating how conifers change their hydraulic architecture during xylem development across the growing season may shed light on possible mechanisms underlying hydraulic adaptation in conifers. Laser microscopy was used to assess the three-dimensional hydraulic architecture of balsam fir ( Abies balsamea (Linnaeus) Miller), jack pine ( Pinus banksiana Lambert), white spruce ( Picea glauca (Moench) Voss), and black spruce ( Picea mariana (Miller) Britton, Sterns & Poggenburgh) seedlings. We measured hydraulic-related xylem traits in different regions (from early to latewood), during four years of plant growth. The xylem development of jack pine seedlings contrasts with the other species by maintaining torus overlap (a hydraulic safety-associated xylem trait), relatively constant across the season (from early to latewood), and over the years studied. The expansion of tracheids and torus was positively associated with plant growth. Pit aperture-torus covariance is central to the seasonal dynamics of jack pine xylem development, which jointly with a rapid tracheid and pit expansion is consistent with strong growth performance. Linking xylem structural changes during xylem development with hydraulics is a major issue for future research to assess conifers vulnerability to 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 | 0.000 | 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) | 0.001 | 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".