Leaf stoichiometry of common species along altitude gradients in the Qilian Mountains, China
Bibliographic record
Abstract
Abstract Investigating the variations in leaf stoichiometry among plant common species at different altitudes, along with the factors that influence these variations and the adaptative strategies employed, is of significant importance for understanding biogeochemical cycles amidst global environmental changes. In this research, we measured soil organic carbon and nutrient concentrations, as well as leaf stoichiometry for plant common species at five altitudes (2400–3200 m with an interval of 200 m) within the Qilian Mountains of Northwest China. This study aims to enhance our understanding of how plant common species in mountainous regions exhibit adaptable responses to altitude variations and how potential environmental changes in the future may influence their leaf functions. Results showed that the leaf C:N:P stoichiometry of plant common species varied with increasing altitude. Across altitudes, mean annual temperature (MAT), soil total phosphorus, mean annual precipitation (MAP), soil water content, and soil nitrate nitrogen were the main factors influencing leaf element concentrations of plant common species. However, leaf stoichiometric ratios were mainly determined by MAT, MAP, and soil total nitrogen. The effects of MAT and MAP on both leaf element concentrations and leaf stoichiometric ratios of plant common species were found to be significant. Plant growth in the study area was mainly limited by P. The results not only highlight the adaptive strategies employed by plants, but also contribute to understanding of leaf stoichiometry, and establishing connections between individual plant species and broader plant community composed of these common species.
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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".