Spatial Distribution of Soil P And its Correlation with Soil Acidity in Mountain Meadow of Wugong Mountain
Bibliographic record
Abstract
Phosphorus is one of the essential elements for plant growth. Sites were set according to different altitudes( the altitude range from 1 600-1 900 m) and different soil depths in the mountain meadow of Wugong Mountain in Jiangxi Province,and the spatial distribution of soil total P content and the available P content,and the correlation between soil P and soil acidity were analyzed. The main research results were as follows:( 1) The cotents of soil available P and total P showed a law of vertical distribution. With the elevation increasing gradually,the total P content and available P content increased gradually,the ranges of variation in soil total P and available P were 3. 71-17. 78 mg / kg and 0. 46-1. 37 g / kg respectively. The available P content had spatial correlation with the total P content.( 2) There was no significant difference in pH along the altitudes and soil depths. The soil total hydrolytic acidity and soil exchangeable acidity increased significantly with the increase of altitude.( 3) There was a significant positive correlation between soil available P and soil total hydrolytic acidity and soil exchangeable acidity. This study revealed the spatial distribution of soil P and the correlation between available P and soil acidity,providing a guidance for vegetation restoration in mountain meadow ecosystem.
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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 | 0.001 |
| 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".