Effects of Age and Size on Xylem Phenology in Two Conifers of Northwestern China
Bibliographic record
Abstract
The climatic signals that directly affect the trees can be registered by xylem during its growth. If the timings and duration of xylem formation change, xylogenesis can occur under different environmental conditions and subsequently be subject to different climatic signals. An experimental design was applied in the field to disentangle the effects of age and size on xylem phenology, and it challenges the hypothesis that the timings and dynamics of xylem growth are size-dependent. Intra-annual dynamics of xylem formation were monitored weekly during the growing seasons 2013 and 2014 in Chinese pine (Pinus tabulaeformis) and Qilian juniper (Juniperus przewalskii) with different sizes and ages in a semi-arid region of northwestern China. Cell differentiation started three weeks earlier in 2013 and terminated one week later in 2014 in small-young pines than in big-old pines. However, differences in the timings of growth reactivation disappeared when comparing the junipers with different sizes but similar age. Overall, 77 days were required for xylem differentiation to take place, but timings were shorter for older trees, which also exhibited smaller cell production. Results from this study suggest that tree age does play an important role in timings and duration of growth. The effect of age should also be considered to perform reliable responses of trees to climate.
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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".