Spatial autocorrelation analysis on soil moisture of Melica przewalskyi patch in a degraded alpine grassland of Qilian Mountains,Northwest China
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Bibliographic record
Abstract
A prerequisite in using conventional statistical methods,such as regression models in investigating spatial distribution of soil moisture,is that the data regarding soil moisture should be statistically independent and identically distributed. However,soil moisture generally exists with spatial autocorrelation to some degree,which contains some useful information. In this paper,the spatial autocorrelation analysis of soil moisture in Melica przewalskyi patch was investigated based on Moran's I index on the north slope of the Qilian Mountains. Moran's I was applied to describe spatial autocorrelation of soil moisture,and analyze the scales of spatial autocorrelation. Meanwhile,standard multiple linear regression model and spatial autoregressive model of soil moisture were constructed. The results showed that distribution of surface soil moisture all displayed spatial autocorrelation characteristics. In addition,the spatial aggregation characteristics of the 20- 30 cm depth were higher than that of the 0- 10 and 10- 20 cm depths. It was found that the Moran's I decreased with the increase of the scale of spatial analysis. The spatial autocorrelation of surface soil moisture resulted from different soil depths. At the 10- 20 cm depth, the community height and Melica przewalskyi coverage had significant effects on the spatial autocorrelation,while at the 20- 30 cm depth,the Stipa krylovii coverage and community height significantly affected the spatial autocorrelation. Our analysis showed that spatial autoregressive model was better than the standard multiple linear regression model due to the spatial autocorrelation exerting more impact on the latter one.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it