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The gut microbiomes of the host are large and complex communities, which helps to maintain homeostasis, improves digestive efficiency, and promotes the development of the immune system. The small mammals distributed in Sichuan Province are the most popular species for biodiversity research in Southwest China. However, the effects of different diets on the structure and function of the gut microbial community of these small mammals are poorly understood. In this study, whole‐metagenome shotgun sequencing has been used to analyze the composition and functional structures of the gut microbiota of seven small mammals in Laojunshan National Nature Reserve, Sichuan Province, China. Taxonomic classification revealed that the most abundant phyla in the gut of seven small mammals were Bacteroides, Proteobacteria, and Firmicutes. Moreover, Hafnia, Lactobacillus, and Yersinia were the most abundant genus in the gut microbiomes of these seven species. At the functional level, we annotated a series of KEGG functional pathways, six Cazy categories, and 46,163 AROs in the gut microbiomes of the seven species. Comparative analysis found that the difference in the gut microbiomes between the Soricidea and Muridae concentrated on the increase in the F/B (Firmicutes/Bacteroides) ratio in the Soricidea group, probably driven by the high‐fat and ‐calorie digestive requirements due to their insectivorous diet. The comparative functional profiling revealed that functions related to metabolism and carbohydrates were significantly more abundant in Muridae group, which may be attributed to their high carbohydrate digestion requirements caused by their herbivorous diet. These data suggested that different diets in the host may play an important role in shaping the gut microbiota, and lay the foundation for teasing apart the influences of heritable and environmental factors on the evolution of gut microbial communities. [ABSTRACT FROM AUTHOR]

Total organic carbon (TOC) content is significant for "sweet spots" prediction and resource calculation. Traditional physical methods and machine learning methods, however, only use labeled samples (core TOC samples and well logging data) to predict TOC, resulting in a performance bottleneck. Meanwhile, many oilfields have considerable unlabeled data (well logging data). Therefore, this paper proposed a pretrain-and-finetune few-shot learning framework, called Contrastive Learning-Convolutional Neural Network (CL-CNN), to predict TOC using unlabeled data. CL has been a research hotspot in unsupervised learning in recent years, with the principle of reducing the distance between similar samples and increasing the distance between different samples in the feature space. CNN is distinguished by its strong generalization ability and high prediction accuracy, as well as its local connectivity and weight sharing. The CL-CNN model first uses CL to pretrain the initial parameters of the CNN model on a large number of unlabeled datasets and then finetunes the resultant CNN model using the labeled data. The framework combines the advantages of CL and CNN to further improve the prediction accuracy and generalization ability. The proposed method was applied to predict the TOC of shale gas in the Sichuan Basin, China, using 121 core samples and 1224 well logging data from the JY 10 well (121 logging data for supervised learning and 1103 logging data for unsupervised learning). Besides, JY 10–10 well data were used to validate the CL-CNN model. Experimental results demonstrated that the CL-CNN model outperformed many machine learning methods, including the support vector machine (SVM), Gaussian process regression (GPR), random forest (RF), and CNN models, indicating the feasibility and effectiveness of the proposed method. Because of this, the CL-CNN model can also be used as a general prediction tool for reservoir parameters such as porosity and permeability. • Contrastive learning was used to learn the general attributes of the unlabeled data. • Contrastive learning-convolutional neural network (CL-CNN) was proposed to predict TOC content. • Unsupervised pretraining and supervised finetuning were used for few-shot TOC prediction. • CL-CNN obtained promising results for predicting TOC and outperformed many methods. • The plane distribution of TOC in the study area was predicted by CL-CNN accurately. [ABSTRACT FROM AUTHOR]