[目的/意义]目前关于论文被引频次影响因素的研究存在多数考察单一因素、忽视期刊影响力控制和缺少期刊及学科间异同比较等问题。在人为控制期刊影响力因素的前提下,探究不同期刊刊载论文被引频次的影响因素并总结共性因素和比较差异性...[目的/意义]目前关于论文被引频次影响因素的研究存在多数考察单一因素、忽视期刊影响力控制和缺少期刊及学科间异同比较等问题。在人为控制期刊影响力因素的前提下,探究不同期刊刊载论文被引频次的影响因素并总结共性因素和比较差异性。[方法/过程]以Web of Science数据库中15本分属不同学科类别的代表性期刊刊载论文为样本,利用回归分析方法,对被研究较多的8个论文被引频次可能的影响因素分别进行实证检验。[结论/结果]在控制期刊影响因子的前提下,各学科期刊影响论文被引频次的因素既有共性,又有差异性。参考文献的引用半衰期在15本期刊中都对被引频次有显著负影响,参考文献数量在大多数期刊中也与被引频次显著正相关,论文篇幅在所有期刊中都不会影响被引频次,其他因素在不同学科期刊中对被引频次的影响差别较大。结合实证结果,对科研工作者如何利用论文外在因素来产出高被引论文提出相应建议,并对使用被引频次来评价论文质量的合理性进行探讨。展开更多
【目的】探究参考文献各指标与论文被引频次之间的关系,为撰写高被引论文、科学评价计量学指标等提供参考。【方法】以2013年Web of Science中凝聚态物理学科下的12种期刊共计8847篇论文为样本数据,利用负二项回归模型测度了参考文献的...【目的】探究参考文献各指标与论文被引频次之间的关系,为撰写高被引论文、科学评价计量学指标等提供参考。【方法】以2013年Web of Science中凝聚态物理学科下的12种期刊共计8847篇论文为样本数据,利用负二项回归模型测度了参考文献的4类指标与论文被引频次之间的关系。【结果】在控制期刊影响因子的前提下,12种期刊的代表时间维度的普赖斯指数都与论文被引频次之间存在显著的正相关关系;在大多数期刊中,参考文献的数量和表征跨学科性的香农指数也对论文的被引频次有显著正影响;而表征论文质量的篇均参考文献被引频次中值与论文被引频次之间的相关关系不明确。【结论】参考文献的一些指标可能影响论文的被引频次,结合实证结果,对科研工作者合理利用参考文献产出高质量论文提出相应建议。展开更多
Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,w...Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,which requires a large number of high-quality training set.To solve this problem,we propose a self-supervised learning method to learn the deep neural network from unlabelled hyperspectral data for oil spill detection,which consists of three parts:data augmentation,unsupervised deep feature learning,and oil spill detection network.First,the original image is augmented with spectral and spatial transformation to improve robustness of the self-supervised model.Then,the deep neural networks are trained on the augmented data without label information to produce the high-level semantic features.Finally,the pre-trained parameters are transferred to establish a neural network classifier to obtain the detection result,where a contrastive loss is developed to fine-tune the learned parameters so as to improve the generalization ability of the proposed method.Experiments performed on ten oil spill datasets reveal that the proposed method obtains promising detection performance with respect to other state-of-the-art hyperspectral detection approaches.展开更多
文摘[目的/意义]目前关于论文被引频次影响因素的研究存在多数考察单一因素、忽视期刊影响力控制和缺少期刊及学科间异同比较等问题。在人为控制期刊影响力因素的前提下,探究不同期刊刊载论文被引频次的影响因素并总结共性因素和比较差异性。[方法/过程]以Web of Science数据库中15本分属不同学科类别的代表性期刊刊载论文为样本,利用回归分析方法,对被研究较多的8个论文被引频次可能的影响因素分别进行实证检验。[结论/结果]在控制期刊影响因子的前提下,各学科期刊影响论文被引频次的因素既有共性,又有差异性。参考文献的引用半衰期在15本期刊中都对被引频次有显著负影响,参考文献数量在大多数期刊中也与被引频次显著正相关,论文篇幅在所有期刊中都不会影响被引频次,其他因素在不同学科期刊中对被引频次的影响差别较大。结合实证结果,对科研工作者如何利用论文外在因素来产出高被引论文提出相应建议,并对使用被引频次来评价论文质量的合理性进行探讨。
文摘【目的】探究参考文献各指标与论文被引频次之间的关系,为撰写高被引论文、科学评价计量学指标等提供参考。【方法】以2013年Web of Science中凝聚态物理学科下的12种期刊共计8847篇论文为样本数据,利用负二项回归模型测度了参考文献的4类指标与论文被引频次之间的关系。【结果】在控制期刊影响因子的前提下,12种期刊的代表时间维度的普赖斯指数都与论文被引频次之间存在显著的正相关关系;在大多数期刊中,参考文献的数量和表征跨学科性的香农指数也对论文的被引频次有显著正影响;而表征论文质量的篇均参考文献被引频次中值与论文被引频次之间的相关关系不明确。【结论】参考文献的一些指标可能影响论文的被引频次,结合实证结果,对科研工作者合理利用参考文献产出高质量论文提出相应建议。
基金supported by the National Natural Science Foundation of China (Grant No. 61890962 and 61871179)the Scientific Research Project of Hunan Education Department (Grant No. 19B105)+3 种基金the Natural Science Foundation of Hunan Province (Grant Nos. 2019JJ50036 and 2020GK2038)the National Key Research and Development Project (Grant No. 2021YFA0715203)the Hunan Provincial Natural Science Foundation for Distinguished Young Scholars (Grant No. 2021JJ022)the Huxiang Young Talents Science and Technology Innovation Program (Grant No. 2020RC3013)
文摘Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,which requires a large number of high-quality training set.To solve this problem,we propose a self-supervised learning method to learn the deep neural network from unlabelled hyperspectral data for oil spill detection,which consists of three parts:data augmentation,unsupervised deep feature learning,and oil spill detection network.First,the original image is augmented with spectral and spatial transformation to improve robustness of the self-supervised model.Then,the deep neural networks are trained on the augmented data without label information to produce the high-level semantic features.Finally,the pre-trained parameters are transferred to establish a neural network classifier to obtain the detection result,where a contrastive loss is developed to fine-tune the learned parameters so as to improve the generalization ability of the proposed method.Experiments performed on ten oil spill datasets reveal that the proposed method obtains promising detection performance with respect to other state-of-the-art hyperspectral detection approaches.
