Background: In this study, we focus on building a fine-grained entity annotation corpus with the corresponding annotation guideline of traditional Chinese medicine (TCM) clinical records. Our aim is to provide a basis for the fine-grained corpus construction of TCM clinical records in future. Methods: We developed a four-step approach that is suitable for the construction of TCM medical records in our corpus. First, we determined the entity types included in this study through sample annotation. Then, we drafted a fine-grained annotation guideline by summarizing the characteristics of the dataset and referring to some existing guidelines. We iteratively updated the guidelines until the inter-annotator agreement (IAA) exceeded a Cohen's kappa value of 0.9. Comprehensive annotations were performed while keeping the IAA value above 0.9. Results: We annotated the 10,197 clinical records in five rounds. Four entity categories involving 13 entity types were employed. The final fine-grained annotated entity corpus consists of 1104 entities and 67,799 tokens. The final IAAs are 0.936 on average (for three annotators), indicating that the fine-grained entity recognition corpus is of high quality. Conclusions: These results will provide a foundation for future research on corpus construction and named entity recognition tasks in the TCM clinical domain.
Recent rapid population growth and increasing urbanisation have led to fast vertical developments in urban areas. Therefore, in the context of the dynamic property market, factors related to the third dimension (3D) need to be considered. Current hedonic price modelling (HPM) studies have little explicit consideration for the third dimension, which may have a significant influence on modelling property values in complex urban environments. Therefore, our research aims to narrow the cognitive gap of the missing third dimension by assessing both 2D and 3D HPM and identifying important 3D factors for spatial analysis and visualisation in the selected study area, Xi’an, China. The statistical methods we used for 2D HPM are ordinary least squares (OLS) and geographically weighted regression (GWR). In 2D HPM, they both have very low R2 (0.111 in OLS and 0.217 in GWR), showing a very limited generalisation potential. However, a significant improvement is observed when adding 3D factors, namely view quality, sky view factor (SVF), sunlight and property orientation. The obtained higher R2 (0.414) shows the importance of the third dimension or—3D factors for HPM. Our findings demonstrate the necessity to include such factors into HPM and to develop 3D models with a higher level of details (LoD) to serve more purposes such as fair property taxation.
The restrictive factors of cultivated land are key to the improvement of cultivated land quality, scientific implementation of the land consolidation projects, and the efficiency of remediation. On the basis of the provincial plots of cultivated land quality in Shaanxi Province, this paper analysed the improvement potential of cultivated land quality from the perspective of restrictive factors. First, the potential exponential model was used to determine the distribution of various combinations of restrictive factors at the provincial scale. Second, a geological detector was used to determine the influences of different combinations of restrictive factors on cultivated land quality. Finally, through the investigation of cultivated land consolidation projects that have been implemented in the study area, the improvement potential level of different combinations of restrictive factors was determined. The degree of influence of the single restrictive factor or combinations of restrictive factors on the quality of cultivated land was improved, and the difference of the quality of cultivated land in different index areas could be revealed as well. The results showed that there were 12 single‐factor restrictions and 34 double‐factor restrictions. The area under single‐factor restrictions reached 76.77% of the total land. The quality of cultivated land in the southern and central areas of Shaanxi Province was relatively good. The quality of cultivated land in the northern region was under significant influence of restrictive factors whereas that in southern and middle areas was less affected. From the perspective of improvement potential of restrictive factors, Shaanxi was relatively low with huge internal diversity, whereas the improvement potential in northern Shaanxi had huge advantage.
Based on the previous research in loess hilly region of Northern Shaanxi, this paper takes the soil erosion degree as the main measure of soil stability and the soil utility, annual average rainfall in flood season (from June to September), and topography, as the main measure indexes of soil stability. After that, the evaluation system of soil stability in loess hilly region of Northern Shaanxi can be constructed, which can be done by special analysis of GIS. The results illustrate that the soil stability showed a trend of high south‐east and low north‐west. Soil with good stability is mainly distributed in Ganquan County, and Yanchuan County, where vegetation coverage is high, vegetation types are mostly forest land and grassland, ecological environment is good, and precipitation erosion effect is not significant. Soil with intermediate stability is mainly distributed in Baota district and its surrounding areas, where the main vegetation types are bush fallow and grassland, and the terrain is flat and gently rolling. Soil with the worst stability is mainly distributed in Suide County, and Wuqi County. The area is mostly sandy and desert, the terrain is fragmented, soil is loose, vegetation cover is not high, making the soil the worst soil stability, and strong rainfall conditions are prone to soil erosion. The prerequisite of the implementation of soil consolidation projects is having evaluation on soil stability. The research results can be the theoretical evidence, and implement guarantee of regional soil exploitation and reorganization, and the reference to enhancing the assurance of ecological safety.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.