Effects of nitrogen addition and increased precipitation on xylem growth of<i>Quercus acutissima</i>Caruth. in central China
Bibliographic record
Abstract
Atmospheric nitrogen (N) deposition and increasing precipitation affect carbon sequestration in terrestrial ecosystems, but how these two concurrent global change variables affect xylem growth in trees (i.e., independently or interactively) remains unclear. We conducted novel experiments in central China to monitor the xylem growth in a dominant species (Quercus acutissima Caruth.) in response to N addition (CN), supplemental precipitation (CW) or both treatments (CNW), compared with untreated controls (C). Measurements were made at weekly intervals during 2014-15. We found that supplemental precipitation significantly enhanced xylem growth in the dry spring of 2015, indicating a time-varying effect of increased precipitation on intra-annual xylem growth. Elevated N had no significant effect on xylem increment, xylem growth rate, and lumen diameters and potential hydraulic conductivity (Ks) of earlywood vessels, but Ks with elevated N was significantly negatively related to xylem increment. The combination of additional N and supplemental precipitation suppressed the positive effect of supplemental precipitation on xylem increment in the dry spring of 2015. These findings indicated that xylem width was more responsive to supplemental precipitation than to increasing N in a dry early growing season; the positive effect of supplemental precipitation on xylem growth could be offset by elevated N resources. The negative interactive effect of N addition and supplemental precipitation also suggested that increasing N deposition and precipitation in the future might potentially affect carbon sequestration of Q. acutissima during the early growing season in central China. The effects of N addition and supplemental precipitation on tree growth are complex and might vary depending on the growth period and local climatic conditions. Therefore, future models of tree growth need to consider multiple-time scales and local climatic conditions when simulating and projecting global change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".