Ammonia Volatilization from Soils Fertilized with Different Nitrogen Type and Application Method in Germination and Early Seedling Stages from the Radish Field

Ammonia volatilization(AV) from basal fertilizer with different nitrogen(N) types and application methods was investigated by the ventilation method in germination and early seedling stages during radish growth seasons in 2014. Four N fertilizer types, urea(U), ammonium bicarbonate(AB), ammonia sulfate(AS), and controlled urea formaldehyde(CUF) were applied through 5 cm depth placement(I) and 10 cm depth placement(II). The results showed that the N fertilizer type was the main factor that caused AV loss in germination and early seedling stages from the radish field. The highest and the lowest cumulative AV losses in germination and early seedling stages from the radish fields were 33.23 and 11.21 N kg/hm^2 for the treatments of AB+I and CUF+II, respectively, accounting for 60.40 and 26.40% of the N application for each treatment. The 10 cm deep placement of N reduced AV rates and lagged the AV process, and CUF significantly reduced ammonia volatilization. The data showed that the suitable N fertilizer type and application method for basal fertilizer were CUF and deep placement, respectively.Therefore, fertilizing with proper N fertilizer types and methods should be the efficient measures to mitigate AV losses from the radish field and will alleviate environment problems.

Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.