Biogeographic patterns of nutrient resorption from <i><scp>Q</scp>uercus variabilis </i><scp>B</scp>lume leaves across <scp>C</scp>hina
Bibliographic record
Abstract
The variation in nutrient resorption has been studied at different taxonomic levels and geographic ranges. However, the variable traits of nutrient resorption at the individual species level across its distribution are poorly understood. We examined the variability and environmental controls of leaf nutrient resorption of Quercus variabilis, a widely distributed species of important ecological and economic value in China. The mean resorption efficiency was highest for phosphorus (P), followed by potassium (K), nitrogen (N), sulphur (S), magnesium (Mg) and carbon (C). Resorption efficiencies and proficiencies were strongly affected by climate and respective nutrients concentrations in soils and green leaves, but had little association with leaf mass per area. Climate factors, especially growing season length, were dominant drivers of nutrient resorption efficiencies, except for C, which was strongly related to green leaf C status. In contrast, green leaf nutritional status was the primary controlling factor of leaf nutrient proficiencies, except for C. Resorption efficiencies of N, P, K and S increased significantly with latitude, and were negatively related to growing season length and mean annual temperature. In turn, N, P, K and S in senesced leaves decreased with latitude, likely due to their efficient resorption response to variation in climate, but increased for Mg and did not change for C. Our results indicate that the nutrient resorption efficiency and proficiency of Q. variabilis differed strongly among nutrients, as well as growing environments. Our findings provide important insights into understanding the nutrient conservation strategy at the individual species level and its possible influence on nutrient cycling.
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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) | 0.001 | 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".