No abstract

No abstract

Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations. Deep analysis of citation may reveal interesting findings by utilizing deep neural network algorithms. The existing scholarly datasets are best suited for statistical approaches but lack citation context, intent, and section information. Furthermore, the datasets are too small to be used with deep learning approaches. For citation intent analysis, the datasets must have a citation context labeled with different citation intent classes. Most of the datasets either do not have labeled context sentences, or the sample is too small to be generalized. In this study, we critically investigated the available datasets for citation intent and proposed an automated citation intent technique to label the citation context with citation intent. Furthermore, we annotated ten million citation contexts with citation intent from Citation Context Dataset (C2D) dataset with the help of our proposed method. We applied Global Vectors (GloVe), Infersent, and Bidirectional Encoder Representations from Transformers (BERT) word embedding methods and compared their Precision, Recall, and F1 measures. It was found that BERT embedding performs significantly better, having an 89% Precision score. The labeled dataset, which is freely available for research purposes, will enhance the study of citation context analysis. Finally, It can be used as a benchmark dataset for finding the citation motivation and function from in-text citations.

Cotton is a vital fiber and oilseed crop badly affected by soil moisture deficit stress. Screening of cotton germplasm is a prerequisite to classify the cotton genotypes as a drought sensitive and tolerant. With the aim to separate the distinct genotypes, eight cotton genotypes and FH Lalazar) were grown under normal moisture (08 irrigations/ 28 acre inches) and moisture stress (03 irrigations at 60, 90 and 120 DAS/ water deficit of 15 inches) environments. The assessment was done through different physiological parameters (stomatal conductance, osmotic and water potential, photosynthetic rate, relative water contents and canopy temperature). Cell injury and yield were also evaluated. The experimental design was Randomized Complete Block Design (RCBD) in factorial way with three replicates. The data obtained was investigated statistically at 5% probability and Least Significant Difference (LSD) test was used to isolate the significant means (treatment). The results indicated that water stress adversely reduced the values of all the above stated parameters. The cotton genotype BH-190 had significantly greater (p<0.05) yield and physiological attributes and found extra water deficit stress tolerant. While cotton genotype FH Lalazar had least value of these attributes as compared to all remaining cotton genotypes and thus considered as water deficit stress sensitive genotype. So, it can be concluded from the results of this experiment that the physiological screening of low soil moisture stress tolerant varieties could be a superior way to mitigate impact of drought stress on the cotton cultivated in drought susceptible regions.