Leaf Stoichiometry of Potentilla fruticosa Across Elevations in China’s Qilian Mountains
Bibliographic record
Abstract
As an individual plant species can develop its own leaf stoichiometry to adapt to environmental changes, this stoichiometry can provide critical information about a plant species’ growth and its potential management in the ecosystem housing it. However, leaf stoichiometry is largely undocumented in regions with large environmental changes arising from differences in elevation. The leaf stoichiometry ofPotentilla fruticosaL., a major alpine shrub playing an important role in supporting ecosystem functions and services in China’s Qilian Mountains (Northeast Qinghai–Tibetan Plateau), was investigated at different elevations (2,400, 2,600, 2,800, 3,000, 3,200, 3,500, and 3,800 m). At each elevation, leaf elemental (C, N, and P) concentrations were measured inP. fruticosaleaves sampled from three plots (10 × 10 m), and edaphic properties were assessed in nine quadrats (1 × 1 m, three quadrats per plot). Temperature and precipitation were calculated using an empirical formula. Maximum and minimum leaf carbon (C) concentrations ([C]leaf) of 524 ± 5.88 and 403 ± 3.01 g kg–1were measured at 2,600 and 3,500 m, respectively. Leaf nitrogen (N) concentration ([N]leaf) showed a generally increasing trend with elevation and peaked at 3,500 m (27.33 ± 0.26 g kg–1). Leaf phosphorus (P) concentration ([P]leaf) varied slightly from 2,400 to 3,200 m and then dropped to a minimum (0.60 ± 0.10 g kg–1) at 3800 m. The [C]leaf:[N]leaf, [C]leaf:[P]leaf, and [N]leaf:[P]leafvaried little from 2,400 to 3,000 m but fluctuated somewhat at higher elevations. The main factors affectingP. fruticosaleaf stoichiometry were soil organic C, pH, and soil total P, and the main limiting element for the growth ofP. fruticosain the study area was P. In conclusion, changes in elevation affected leaf stoichiometry ofP. fruticosamainly due to altered soil properties, and addressing phosphorus limitation, especially at higher elevations mainly due to losses caused by high precipitation and sparse vegetation, is a key measure to promoteP. fruticosagrowth in this region.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".