Seasonal Dynamics of Leaf Stoichiometry of Phragmites australis: A Case Study From Yangguan Wetland, Dunhuang, China
Bibliographic record
Abstract
Leaf stoichiometry can enhance our understanding of leaf elements’ (C, N and P) concentrations and their corresponding ratios in an ecosystem with seasonal environment changes. This study quantified the seasonal dynamics of leaf stoichiometry of P. australis from Yangguan wetland, Dunhuang, China as a case study example. The leaf C concentration (LC) of P. australis changed between seasons and was 392.26 (g·kg−1), 417.35 (g·kg−1) and 392.58 (g·kg−1) in spring, summer and autumn, respectively. Leaf N and P concentrations (LN and LP) were 23.49 (g·kg−1), and 17.54 (g·kg−1) and 5.86 (g·kg−1), and 1.00 (g·kg−1), 0.75 (g·kg−1) and 0.16 (g·kg−1), respectively, in the three seasons. The maximum (77.68) and the minimum values (17.00) of LC:LN were observed in the autumn and spring, respectively. Seasonal variations in LC:LP also showed a similar trend, with the greatest value of 3015.91 in autumn and the lowest value of 429.39 in spring. However, the highest (45.67) and the lowest values (24.18) of LN:LP were observed in autumn and summer, respectively, indicating that the growth of P. australis was mainly affected by P. Based on these results, it can be concluded that P. australis adopted a competition strategy during the early growth stage but took on a defense life strategy at the late growth stage to cope with various environments.
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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) | 0.001 | 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 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".