Leaf phosphorus content of Quercus wutaishanica increases with total soil potassium in the Loess Plateau
Bibliographic record
Abstract
Phosphorus (P) is arguably more limiting than nitrogen for forest ecosystems being free of disturbances for lengthy time periods. The elucidation of multivariate relationships between foliar P and its primary drivers for dominant species is an urgent issue and formidable challenge for ecologists. Our goal was to evaluate the effects of primary drivers on foliar P of Quercus wutaishanica, the dominant species in broadleaved deciduous forest at the Loess Plateau, China. We sampled the leaves of 90 Q. wutaishanica individuals across broad climate and soil nutrient gradients at the Loess Plateau, China, and employed structural equation models (SEM) to evaluate multiple causal pathways and the relative importance of the drivers for foliar P per unit mass (Pmass) and per unit area (Parea). Our SEMs explained 73% and 81% of the variations in Pmass and Parea, respectively. Pmass was negatively correlated to leaf mass per area, positively correlated to leaf area, and increased with mean annual precipitation and total soil potassium. Parea was positively correlated to leaf mass per area, leaf dry weight, and increased significantly with total soil potassium. Our results demonstrated that leaf P content of Q. wutaishanica increased with total soil potassium in the Loess Plateau accordingly.
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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 | 0.000 | 0.000 |
| 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".