Trend Effect and an Isotropy of Soil Particle Composition in the Chengdu Plain
Bibliographic record
Abstract
This paper studies the spatial variation characteristics of soil particle composition on both sides of Qingbaijiang River (Chengdu Xin Du District of Sichuan province) by using the geostatistical component of ArcGIS and GS software. The results have shown that the trends of sand contents in the east-west direction and the north-south direction were first order and second order, respectively. And the trends of both silt particle and clay particle contents were first order in the east-west direction, and second order in the north-south direction. The anisotropy semi variance models of sand particle content showed that the ranges in the long axis direction were 718.77 m and 677.01 m and the ranges in the short axis direction were 273.78 m and 276.63 m with first order and second order. The results showed that the difference between the ranges in the short axis direction and the ranges in long axis direction changed little, while the anisotropy semi variance model had longer ranges than the isotropy model with the trend parameters of first order. From the error analysis and the result that reflect regional and local trend, it indicated that Kriging interpolation method considering the study about soil particle composition along Qingbaijiang River with the second order trend effect was the best, comparing the contour maps of sand particle content under different trend effects and anisotropy parameters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".