Using Leaf Ecological Stoichiometry to Direct the Management of Ligularia virgaurea on the Northeast Qinghai-Tibetan Plateau
Bibliographic record
Abstract
Leaf ecological stoichiometry not only reflects the plasticity and adaptability, but also the growth of plants within environments where temperature, precipitation, and soil properties vary across an elevation gradient. Ligularia virgaurea (Maxim.) Mattf. ex Rehder & Kobuski — an invasive poisonous plant — is common in the northeast portion of China’s Qinghai-Tibetan Plateau and its presence greatly affects the native ecosystem. Based on L. virgaurea leaf carbon ([C]leaf), nitrogen ([N]leaf) and phosphorus ([P]leaf) concentrations, and their ratios, the species’ coping strategies across an elevation gradient (2,600 m, 3,000 m, and 3,300 m) were identified, and served to inform the development of improved management strategies. Mean [C]leaf, [N]leaf and [P]leaf in L. virgaurea across all elevations were 413.14 g·kg−1, 22.76 g·kg−1, and 1.34 g·kg−1, respectively, while [C]leaf: [N]leaf, [C]leaf: [P]leaf, and [N]leaf: [P]leaf were 18.27, 328.76, and 17.93. With an increase in precipitation and decrease in temperature from 2,600 m to 3,000 m–3,300 m, [C]leaf, [C]leaf: [N]leaf and [C]leaf: [P]leaf of L. virgaurea decreased at first and then increased. The [N]leaf and [P]leaf gradually increased, whereas [N]leaf: [P]leaf showed little change. Although temperature, precipitation and soil water content were the main factors affecting the ecological stoichiometry of L. virgaurea leaves, their roles in influencing leaf elements were different. The [C]leaf was mainly influenced by soil water content, [N]leaf by temperature and soil water content, and [P]leaf by all of them. With potential future climate change in the study area, L. virgaurea may grow faster than at present, although soil P may still be a growth-limiting element. As L. virgaurea can reduce plant diversity and the quality of forage, while increasing biomass, management of L. virgaurea should receive greater attention.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".