Role of soil nutrient elements transport on Camellia oleifera yield under different soil types
Bibliographic record
Abstract
Abstract Background Most of C. oleifera forests have low yield and poor quality, largely associating with soil fertility. Soil physical and chemical properties interact with each other to affect soil fertility. C. oleifera growing under different soil conditions affected their yield and oil composition. Three main soil types were selected, and redundancy, correlation, and double-screening stepwise regression analysis were used, for exploring the relationships between Camellia oleifera nutrients uptake and soil physical and chemical properties, shedding light on the transport law of nutrient elements from root, leaves, and kernel, and elaborating the regulation of fruit yield and oil composition. Results In the present study, available soil elements content of C. oleifera forest were mainly regulated by water content, pH value total N, P and Fe contents. Seven elements (N, P, K, Mg, Cu, Mn and C) were key for kernel’s growth and development, with N, P, K, Cu and Mn contents determining the yield traits. The transport characteristics of these nutrients from root, leaves to the kernel were synergistic and antagonistic effects. There were two ways to control the oil production and the content of various oil components: one was to adjust N, P, K, Mg, Zn, Fe and Cu contents of leaves by applying corresponding foliar fertilizers, while the other was to change the soil water content and pH, and apply N, P, Zn, Mg and Ca fertilizers. Conclusion Soil type controlled nutrient absorption by soil pH, water content and total N, P and Fe content. There were synergistic and antagonistic effects on the inter-organ transport of nutrient elements, ultimately affecting the contents of N, P, K, Cu and Mn in kernel, which determined the yield and oil composition of C. oleifera.
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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 | 0.000 |
| 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) | 0.001 | 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".