Intra-annual Dynamics of Xylem Formation in Liquidambar formosana Subjected to Canopy and Understory N Addition
Bibliographic record
Abstract
Increasing N deposition caused by intensive anthropogenic activities is expected to affect forest growth. However, the effects of N deposition on trees are still controversial due to the wide variability in results and experimental methods used. We conducted an experiment involving both canopy and understory N addition to investigate the effects of N-addition on intra-annual xylem formation of Chinese sweetgum (Liquidambar formosana) in a warm-temperate forest of Central China. Since 2013, 50 kg N ha-1 yr-1 (2.5 times the current natural N deposition) was applied monthly from April to December. In 2014 and 2015, timing and dynamics of xylem formation were monitored weekly during March-December by microcoring the stems of control and treated trees. Similar dynamics of wood formation were observed between canopy and understory N addition. Xylem formation of all the experimental trees started in March and lasted for 119-292 days. Compared to the control, no change was observed in the timing and dynamics of wood formation in N-treated trees. Tree ring-width ranged between 1701 and 4774 μm, with a rate of xylem production of 10.52-26.64 μm day-1. The radial growth of trees was not modified by the treatments. Our findings suggest that short-term N addition is unable to affect timings and dynamics of xylem formation in Chinese sweetgum in Central China. The effects of N on tree growth observed in previous studies might be related to the duration of the experiment or the imbalance between the amount of natural deposition and N added during treatments.
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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) | 0.001 | 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".