Biogeographic patterns of multi-element stoichiometry of <i>Quercus variabilis</i> leaves across China
Bibliographic record
Abstract
The variability of leaf stoichiometry has been studied at different taxonomic levels across various geographic ranges. However, the intraspecific variations in leaf stoichiometry of widely distributed species are poorly understood. We characterize the biogeographical patterns and environmental controls of leaf nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S) concentrations of Quercus variabilis Blume, a widely distributed tree species of significant economic and ecological value in China. The stoichiometry of Q. variabilis leaves exhibited substantial variation, which was strongly affected by climatic factors and respective concentrations of soil nutrients but had little association with leaf mass per area. Climate was the dominant driver, apart from P and Ca, which were also strongly related to soil P and Ca, respectively. Concentrations of leaf N, P, K, and Mg decreased significantly with mean annual temperature and increased with aridity, albeit at different magnitudes, resulting in positive latitudinal trends of all elements except for Ca and S. The results indicate that Q. variabilis leaf stoichiometry shows a relevant degree of flexibility and that alterations in climatic factors and soil nutrient availability have diverse influences on patterns of the different elements. Our findings provide an important contribution toward an understanding of how widespread species regulate their stoichiometry to adapt to heterogeneous environments.
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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 | 0.002 | 0.001 |
| 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".