Permeability Characteristics of Soils and Their Dependence on Soil Conditions in Ejina Oasis
Bibliographic record
Abstract
Permeation in soil is an important process in natural water cycle.It has quite relations with the soil conditions.The saturated hydraulic conductivity was measured by using Guelph Permeameter in Ejina Oasis in the lower reaches of Heihe River.The data measured under different conditions were compared.Using the principal component analysis function of statistics software SPSS,the effects of all factors,including soil moisture content,soil texture,soil salinity,soil organic and so on,on soil permeability were analyzed.The soil saturated hydraulic conductivities under different land-use way were calculated out and compared.The order of the conductivities are in West Gobi in the narrow-leaved oleaster forest in Qidaoqiao in the diversiform-leaved poplar forest in Erdaoqiao in the cotton land of Forestry Work Station in the Chinese tamarisk forest in Erdaoqiao in the sacsaoul plant nursery.Firstly,the factor's KMO(Kaiser-Meyer-Olkin) examination was carried out and found that there were 6 factors with KMO of 0.549,which were fitted basically the principal components analysis.Then three principal components,the soil loose degree factor,the soil moisture factor,the bulk specific gravity and the positive and negative ion total quantity factor,were extracted from these influence factors.The cumulative contribution rate of the three principal components was 80.427%.It is found that the soil texture has the maximum influence,and the positive and negative ion total quantity factor,as well as the bulk specific gravity factor,has the minimum influence to the soil infiltration.
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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 | 0.001 | 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".