Impact of drought stress and fertilization on plant traits and nonstructural carbohydrates of Red-Heart Chinese fir
Bibliographic record
Abstract
Understanding how drought stress and fertilization influence plant physiological responses is essential for improving forest management under climate change. Previous research has primarily focused on the effects of drought stress on resource allocation and mortality. However, the interaction effect of fertilization and drought on key plant traits and non-structural carbohydrates (NSCs) dynamics remains uncertain, particularly in Red-Heart Chinese fir ( Cunninghamia lanceolata (Lamb.) Hook.). In this study, the effects of different drought stress gradients and fertilization on aboveground plant traits (different organ biomass, water content, needle number, needle area), belowground plant traits (tap root depth and lateral root spreads), leaf water potential and NSCs were examined in a pot experiment. The trade-offs in growth between aboveground and belowground plant traits become increasingly evident with soil drought gradients. Saplings in the wettest ( W 25min ) and driest ( W 0min ) group in both fertilized and unfertilized groups show clear differentiation along the two principal component axes, which are primarily determined by variations in the number of leaves on branches and leaf predawn water potential. Drought intensity mainly influences the leaf total NSCs, and the drought duration mainly influences the branch total NSCs. Fertilization typically promotes the growth of plants, especially below ground tissues. However, fertilization during drought exacerbated mortality in our experiment, especially for the moderate drought. The work highlights that Red-heart Chinese fir traits respond to drought stress gradients and fertilization, demonstrates that fertilization in combination with drought has an antagonistic effect on the growth and survival of red-heart Chinese fir saplings.
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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".