Water Supply Changes N and P Conservation in a Perennial Grass <i>Leymus chinensis</i>
Bibliographic record
Abstract
Changes in precipitation can influence soil water and nutrient availability, and thus affect plant nutrient conservation strategies. Better understanding of how nutrient conservation changes with variations in water availability is crucial for predicting the potential influence of global climate change on plant nutrient-use strategy. Here, green-leaf nitrogen (N) and phosphorus (P) concentrations, N- and P-resorption proficiency (the terminal N and P concentration in senescent leaves, NRP and PRP, respectively), and N- and P-resorption efficiency (the proportional N and P withdrawn from senescent leaves prior to abscission, NRE and PRE, respectively) of Leymus chinensis (Trin.) Tzvel., a typical perennial grass species in northern China, were examined along a water supply gradient to explore how plant nutrient conservation responds to water change. Increasing water supply at low levels (< 9000 mL/year) increased NRP, PRP and PRE, but decreased green-leaf N concentration. It did not significantly affect green-leaf P concentration or NRE. By contrast, all N and P conservation indicators were not significantly influenced at high water supply levels (> 9000 mL/year). These results indicated that changes in water availability at low levels could affect leaf-level nutrient characteristics, especially for the species in semiarid ecosystems. Therefore, global changes in precipitation may pose effects on plant nutrient economy, and thus on nutrient cycling in the plant-soil systems.
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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".