The species-specific responses of nutrient resorption and carbohydrate accumulation in leaves and roots to nitrogen addition in a subtropical mixed plantation
Bibliographic record
Abstract
Ephemeral tissues such as leaves and fine roots are sensitive to nutrient alteration. Whether nutrient addition can influence the linkage between nutrient resorption and carbohydrate accumulation in leaves and roots is not clear. We measured nitrogen (N) and phosphorus (P) concentrations and nonstructural carbohydrates (NSC) of the one-year-old leaves and absorptive and transportive roots in two species of a mixed plantation during the dormant and growing seasons within an N-addition experiment. Nitrogen addition decreased N and P resorption efficiencies (NRE and PRE, respectively) in leaves of Chinese fir and increased PRE in absorptive roots of Chinese fir but did not alter either efficiency in any tissues of Chinese sweetgum. Nitrogen addition increased starch accumulation efficiency (STAE) in >one-year-old leaves of Chinese fir but decreased soluble sugar accumulation efficiency (SSAE) in absorptive roots of Chinese sweetgum. Both NRE and PRE were negatively correlated with SSAE, STAE, and NSC accumulation efficiency (NSCAE) in >one-year-old leaves of Chinese fir, but this pattern was not found in leaves of Chinese sweetgum. Our study indicates that N addition can influence the linkage between nutrient resorption and NSC in leaves and roots, and this response to nutrient availability is species-dependent.
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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".