SOIL PERMEABILITY OF AERATION ZONE IN XINCHANG-XIANGYANGSHAN- A PRESELECTED SITE FOR HIGH LEVEL RADIOACTIVE WASTE DISPOSAL
Bibliographic record
Abstract
In selecting and evaluating a new dump site for disposal of high-level radioactive waste(HLW)repository,its hydrogeology condition is one of the most important factors. Moreover,soil permeability of the aeration zone is one of the key issues in the study of hydrogeology,affecting water infiltration,runoff and evapotranspiration in the surface soil layer and their distribution relations. Analysis of soil permeability of the aeration zone helps understand the mechanism of groundwater formation. Beishan in Gansu Province is a preselected priority region for disposal of China's HLW,while Xinchang-Xiangyangshan is a preselected priority site. For that end,investigations were carried out on soil permeability of the aeration zone in that preselected site,using the double ring method and and a Guelph infiltration meter in-situ. With the aid of software SPSS 19.0,soil organic matter,initial soil water content,soil p H,soil bulk density,soil total ion content and soil particle size distribution were selected as 6 main affecting factors for principal component analysis. This project was aimed at acquiring data for in-depth study on characteristics of soil water movement and formation and circulation of groundwater in the area,so as to provide some scientific basis for site determination and performance evaluation of the preselected area. The following knowledge and results were obtained:(1)horizontal distribution of permeability coefficients of the soil aeration zone of Xinchang-Xiangyangsan was characterized by 1 soil permeability varied sharply with the location;2 in terms of soil saturated hydraulic conductivity(Kfs),various geomorphic units followed an order of gully(4×10-5 m s-1)slow slope(1.6×10-5 m s-1)flat land(7.69×10-6 m s-1). Therefore,it could be inferred that gullies are the main area where rainwater infiltrates and permeates into deep soil to form groundwater,and an area that should be crossed out from the list of candidates of HLW dump sites,because of its active groundwater;3 Kfs is higher at the Xinchang fault belt than at the Jijicao fault belt,being on average 2.75×10-4 m s-1 and 9.37×10-5 m s-1,respectively. So large-scaled fault belts should also be crossed out from the list due to its highly active groundwater;4 Kfs in clay is very low,being on average only 9.99×10-7 m s-1.(2)Soil permeability also varied vertically,with Kfs rising first and declining later down the soil in depth at the four experimental points in the Sishilijing Basin. As the number of soil samples was limited,causes and trend of the change need to be further explored.(3)Analysis of factors affecting Kfsand its mechanism shows that Kfs is closely related to soil physical and chemical properties,and the principal component analysis indicates that Kfs affecting factors may be summarized as follows:the first principal component may be described as soil looseness,which is affected mainly by initial soil water content,organic matter content and soil particle size composition;the second one is basic physical and chemical properties,mainly including soil p H,soil total ion content,soil bulk density and soil particle size(0.5 mm)composition;and the third one is soil particle size composition,mainly of the particles ranging from 0.1mm to 0.5 mm in size. In a word,among all the affecting factors,the contents of moderate-coarse sands(0.5 mm)and clayey silts(0.1 mm)are the highest in effect affecting Kfs,while the content of medium-fine sands(0.1~0.5 mm)is the lowest.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".