Determinants of the N content of Quercus wutaishanica leaves in the Loess Plateau: a structural equation modeling approach
Bibliographic record
Abstract
Most terrestrial ecosystems are nitrogen (N)-limited. The elucidation of the multivariate relationships among environmental drivers, leaf morphological traits, and foliar N of dominant species which are critical to the functioning of forests remains a critical challenge for ecologists. We sampled leaves of Quercus wutaishanica across a broad natural gradient in the Loess Plateau, China, and employed structural equation modelling to evaluate the causal pathways and the relative importance of drivers of the foliar N per unit area (Narea) and per unit mass (Nmass). We found that (1) Nmass and Narea were primarily affected by leaf morphological traits instead of environmental variables and that leaf morphological traits accounted for most of their variations; (2) the total soil potassium and phosphorus and mean annual precipitation had different effects on Nmass and Narea via different pathways and path coefficients, whereas the mean annual temperature and total soil N had non-significant effects on Nmass and Narea. Our results demonstrated that variations in Nmass and Narea within Quercus wutaishanica were strongly linked to their leaf morphological traits and that the leaf N was also influenced by mean annual precipitation and soil phosphorus and potassium instead of soil N in the Loess Plateau, China.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".