Frontiers in Marine Science最新文献

{"title":"Evaluating RANS and LES turbulence models in hybrid wave modelling of breaking waves","authors":"Chengzhao Zhang, Eugeny Buldakov","doi":"10.3389/fmars.2025.1484783","DOIUrl":"https://doi.org/10.3389/fmars.2025.1484783","url":null,"abstract":"Understanding the characteristics of breaking waves in deep and intermediate waters is crucial for air-sea interactions. Recent advancements in modelling these interactions have often relied on numerical wave tanks using Stokes waves, which may not fully represent real-world conditions. To address this gap, we developed a numerical wave tank to investigate the effects of different turbulence models on the performance of our numerical wave model in simulating breaking waves under more realistic wave conditions. A hybrid model that couples a Lagrangian wave model with a VOF model based on OpenFOAM is developed to simulate breaking wave groups resulting from dispersive focussing, with a spectrum related to a modelled sea state. The numerical results obtained through the hybrid wave model without turbulence models are validated against experimental data, demonstrating a high level of accuracy. Then, four turbulence models including RANS standard <jats:italic>k</jats:italic> − <jats:italic>ϵ</jats:italic>, RNG <jats:italic>k</jats:italic> − <jats:italic>ϵ</jats:italic> models, LES Smagorinsky and LES <jats:italic>k</jats:italic>-equation turbulence models are applied to the hybrid wave model with peak-focussed wide band Gaussian (GW) spectrum. The effects of turbulence models on the prediction of breaking crests, the energy dissipation due to breakers and the estimation of the breaking strength parameter <jats:italic>b</jats:italic> are investigated. The findings demonstrate that the turbulence models can significantly affect the numerical results for weak breaking cases. Notably, the hybrid wave model with the LES <jats:italic>k</jats:italic>-equation turbulence model showed superior performance. This proposed numerical wave tank can be a promising tool for investigating air-sea interactions in 3D simulations under more realistic wave conditions.","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"12 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lightweight YOLO network using temporal features for high-resolution sonar segmentation","authors":"Sen Gao, Wei Guo, Gaofei Xu, Ben Liu, Yu Sun, Bo Yuan","doi":"10.3389/fmars.2025.1581794","DOIUrl":"https://doi.org/10.3389/fmars.2025.1581794","url":null,"abstract":"IntroductionHigh-resolution sonar systems are critical for underwater robots to obtain precise environmental perception. However, the computational demands of processing sonar imagery in real-time pose significant challenges for autonomous underwater vehicles (AUVs) operating in dynamic environments. Current segmentation methods often struggle to balance processing speed with accuracy.MethodsWe propose a novel YOLO-based segmentation framework featuring: (1) A lightweight backbone(ghostnet) network optimized for sonar imagery processing (2) A bypass BiLSTM network for temporal feature learning across consecutive frames. The system processes non-keyframes by predicting semantic vectors through the trained BiLSTM model, selectively skipping computational layers to enhance efficiency. The model was trained and evaluated on a high-resolution sonar dataset collected using an AUV-mounted Oculus MD750d multibeam forward-looking sonar in two distinct underwater environments.ResultsImplementation on Nvidia Jetson TX2 demonstrated significant performance improvements. (1) Processing latency reduced to 87.4 ms (keyframes) and 35.3 ms (non-keyframes)(2)Maintained competitive segmentation accuracy compared to conventional methods and achieved low latency.DiscussionThe proposed architecture successfully addresses the speed-accuracy trade-off in sonar image segmentation through its innovative temporal feature utilization and computational skipping mechanism. The significant latency reduction enables more responsive AUV navigation without compromising perception quality. The newly introduced dataset fills an important gap in high-resolution sonar benchmarking. Future work will focus on optimizing the keyframe selection algorithm and expanding the dataset to include more complex underwater scenarios.","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"58 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning-based detection, classification, and quantification of marine litter along the central east coast of India","authors":"Mallela Pruthvi Raju, Subramanian Veerasingam, V. Suneel, Fahad Syed Asim, Hana Ahmed Khalil, Mark Chatting, P. Suneetha, P. Vethamony","doi":"10.3389/fmars.2025.1604055","DOIUrl":"https://doi.org/10.3389/fmars.2025.1604055","url":null,"abstract":"Globally, the growth of plastic production has increased exponentially from 1.5 million metric tons (Mt) in 1950 to 400.3 Mt in 2022, resulting in a substantial increase of marine litter along the coastal region. Presently, there is a growing interest in using an artificial intelligence (AI) based automatic and cost-effective approach to identify marine litter for clean-up processes. This study aims to understand the spatial distribution of marine litter along the central east coast of India using the conventional method and AI based object detection approach. From the field survey, a total of 4588 marine litter items could be identified, with an average of 1.147 ± 0.375 items/m<jats:sup>2</jats:sup>. Based on clean coast index, 37.5% of beaches were categorized as ‘dirty’ and 62.5% of beaches as ‘extremely dirty’. For the machine learning approach ‘You Only Look Once (YOLOv5)’ model was used to detect and classify various types of marine litter items. A total of 9714 images representing seven categories of marine litter (plastic, metal, glass, fabric, paper, processed wood, and rubber) were extracted from eight field videos recorded across diverse beach settings. The efficiency of the trained machine learning model was assessed using different metrices such as Recall, Precision, Mean average precision (mAP) and F1 score (a metric for forecast accuracy). The model achieved a F1 score of 0.797, mAP 0.5 of 0.95, and mAP@0.5-0.95 of 0.76, and these results show that YOLOv5 model could be used in conjunction with conventional marine litter monitoring, classification and detection to provide quick and accurate results.","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"134 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of sediment contamination and benthic habitat response in mangrove ecosystems of Hainan Province","authors":"Fa Zheng, Zanhui Huang, Zeheng Chen, Jiahui Liu, Mingliang Zhou, Weipin Ding, Xiong Guo, Liang Chen, Zhaofan Wang, Yan Xu","doi":"10.3389/fmars.2025.1542864","DOIUrl":"https://doi.org/10.3389/fmars.2025.1542864","url":null,"abstract":"IntroductionChina's rapid economic growth has led to escalating environmental pollution, significantly impacting mangrove ecosystems. The persistence and response to pollution in mangrove ecosystems involve multiple processes, including the accumulation of contaminants in sediments, their transport in plants, and their accumulation in other organisms. However, comprehensive studies on the multidimensional interactions among these processes are limited.MethodsThis study investigated two mangrove forest areas in Hainan, which were categorized according to the type of mangrove forest cover: planted forests and natural forests. Thirty sampling sites were established to collect data on benthic organisms and their sediment characteristics.ResultsElemental As showed moderate, ongoing pollution. The distribution of species in the two regions showed significant population differences. The benthic population density in the natural forest was significantly lower than that in the planted forest, which was mainly due to the prevalence of Batillaria cumingi, and biodiversity indices and habitats in the natural forest were superior to those in the planted forest, which mainly depended on the degree of anthropogenic disturbance. Total phosphorus, nitrogen, dissolved solids, Hg, and sand grains were the most important variables.DiscussionTotal phosphorus and total nitrogen were the most important environmental factors affecting community composition, while total dissolved solids influenced overall changes in species composition, highlighting the significant influence of the type of mangrove cover on sediment pollution and environmental factors, leading to significant changes in the biomass and density of benthic organisms. This study emphasizes the complex interactions among sediment contamination, mangrove cover, and benthic communities, providing a three-dimensional view of the distribution patterns of mangrove contamination.","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual mobilization of buried microplastics and organic carbon driven by seagrass degradation: a case study from Swan Lake, China","authors":"Yuzhou Huang, Shuo Yu, Zhenming Zheng, Xi Xiao, Zuhao Zhu, Liangchao Deng, Huihua Wei, Jiani Liang, Shuilan Chen, Marianne Holmer","doi":"10.3389/fmars.2025.1593776","DOIUrl":"https://doi.org/10.3389/fmars.2025.1593776","url":null,"abstract":"Seagrass beds are significant sinks for microplastics. However, the degradation of seagrass beds poses significant challenges, and evidence regarding its impacts on microplastic sinks remains scarce. In this study, sediment cores were collected to investigate microplastic stock and composition, microplastic carbon, and organic carbon stock in <jats:italic>Zostera japonica</jats:italic> seagrass bed and adjacent degraded area in a lagoon Swan Lake, China. The microplastic stock in seagrass bed (84.5 ± 18.5 million particles ha<jats:sup>-1</jats:sup>) was found significantly higher than degraded area (51.8 ± 0.6 million particles ha<jats:sup>-1</jats:sup>), resulting in release of 38.7% of buried microplastics reactivated in water column. Similarly, 30.0% of the microplastic carbon stock and 66.1% of the total organic carbon stock were eroded due to seagrass degradation. The carbon stocks derived from microplastics were estimated at 0.19 ± 0.10 kg C ha<jats:sup>-1</jats:sup> in the seagrass bed and 0.13 ± 0.11 kg C ha<jats:sup>-1</jats:sup> in the degraded area, contributing minimally to the total organic carbon stock (0.0023% and 0.0026%, respectively). Notably, seagrass degradation within a single year may trigger rapid erosion of organic carbon and microplastics buried for over 20 years in Swan Lake. A linear relationship was observed between sediment microplastic carbon and total organic carbon contents (Organic carbon = 1990 + 35100 × Microplastic carbon, R² = 0.26, <jats:italic>p</jats:italic> &amp;lt; 0.001). Microplastics in the sediments were predominantly fiber (48.1%), black (40.7%), 250–500 µm (47.0%) microplastics in degraded area, while plate (26.7%), blue and transparent, each contributing 26.7% and 125–250 µm (38.2%) in seagrass bed. Seagrass bed degradation may not only reduce the stock of microplastics in the sediments but also alter their composition. This study initially quantified the contribution of microplastics to organic carbon stocks in seagrass bed sediments and underscored the urgent need for seagrass conservation to mitigate climate change and prevent the remobilization of historically buried microplastics.","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"98 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ship behavior pattern recognition method based on hybrid graph neural networks","authors":"Lin Ma, Hao Cao, Guo-You Shi","doi":"10.3389/fmars.2025.1605216","DOIUrl":"https://doi.org/10.3389/fmars.2025.1605216","url":null,"abstract":"IntroductionAccurate identification of ship behavioral patterns is essential for maritime management, contributing to improved regulatory efficiency, accident prevention, navigation safety, and scheduling. However, traditional methods often struggle with the complexity of high-dimensional, time-series trajectory data.MethodsTo overcome these challenges, this study proposes the following optimized graph neural network (GNN) models: an optimized adjacency matrix graph convolutional network, a hybrid model combining a graph convolutional network with a graph attention network (GAT), and an integrated model of GAT and long short-term memory. These models leverage standardized automatic identification system data to improve feature extraction and recognition accuracy.ResultsExperimental results demonstrate that the proposed models achieve over 98% accuracy in ship behavioral pattern recognition, with fast convergence and superior performance compared to conventional GNN-based methods.DiscussionThe models provide robust and efficient solutions for maritime traffic analysis, offering significant potential for real-world applications in ship monitoring, intelligent navigation, and maritime safety management.","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"40 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate extraction of ocean tidal constituents from coastal satellite altimeter records","authors":"Yanguang Fu, Panlong Wang, Fukai Peng, Yikai Feng, Mehdi Khaki, Xiaolong Mi","doi":"10.3389/fmars.2025.1592765","DOIUrl":"https://doi.org/10.3389/fmars.2025.1592765","url":null,"abstract":"Extracting tidal constituents in coastal regions remains a major challenge due to complex bathymetry, nonlinear shallow-water effects, and land contamination in satellite altimetry measurements. While tide gauges provide high-precision tidal observations, their sparse spatial coverage limits their utility for global coastal studies. Global tidal models, though improved by data assimilation, often suffer from reduced accuracy in coastal zones due to limited spatial resolution and insufficient nearshore constraints. To address these limitations, we utilize the newly released International Altimetry Service 2024 (IAS2024) dataset, which is derived from reprocessed Jason-1/2/3 satellite altimetry data covering the period 2002–2022. We extract ten primary tidal constituents (Q<jats:sub>1</jats:sub>, O<jats:sub>1</jats:sub>, P<jats:sub>1</jats:sub>, K<jats:sub>1</jats:sub>, N<jats:sub>2</jats:sub>, M<jats:sub>2</jats:sub>, S<jats:sub>2</jats:sub>, K<jats:sub>2</jats:sub>, Sa, and Ssa) in global coastal waters using this dataset. The accuracy of IAS2024 tidal extractions is assessed through comparative analysis with four state-of-the-art global tidal models (DTU16, EOT20, FES2014, and FES2022) and 164 tide gauge records. IAS2024 achieves accuracy levels comparable to EOT20 and superior to FES2014 and FES2022, with performance closely matching that of DTU16. For the eight major tidal constituents, the root sum square error of IAS2024 is 11.26 cm, aligning closely with DTU16 (11.23 cm), EOT20 (11.68 cm), and FES2022 (11.26 cm). Relative errors against tide gauge records are 14.16% (O<jats:sub>1</jats:sub>), 16.6% (M<jats:sub>2</jats:sub>), 15.4% (K<jats:sub>1</jats:sub>), and 17.7% (S<jats:sub>2</jats:sub>), demonstrating competitive accuracy. Notably, IAS2024 significantly outperforms traditional models in resolving long-period constituents, with amplitude correlation coefficients of 0.924 for Sa and 0.701 for Ssa, markedly surpassing EOT20 and FES2022. IAS2024 shows strong performance within 10 km of the coast—where conventional altimetry is often unreliable—highlighting its potential for coastal applications. Its enhanced ability to resolve long-period tidal variations makes it particularly valuable for coastal sea level research, tidal energy assessments, and hydrodynamic modeling. These findings underscore the strengths of IAS2024 in nearshore tidal extraction and its importance as a dataset for advancing both global and regional tidal studies.","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"11 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A decade-long perspective on marine health changes in Dalian, China","authors":"Xiaocheng Wang, Yumeng Wang, Haining Wang, Jiangpeng Fan, Mian Li, Wentong Hao","doi":"10.3389/fmars.2025.1532468","DOIUrl":"https://doi.org/10.3389/fmars.2025.1532468","url":null,"abstract":"The ocean, serving as the cradle of life on Earth and the regulator of global climate, constitutes an invaluable resource repository for human survival and development. The preservation of ocean health is pivotal for the sustainable progression of human society. This study chose Dalian in China to assess the marine health using the Ocean Health Index (OHI) at the city-level by selecting appropriate parameters and reference points to align with data sources. The results indicated that the overall OHI score declined by 5.81 in Dalian from 2012 to 2022. For decades, the scores for Clean Waters and Coastal livelihoods and economies had consistently maintained perfection. The scores for Food Provision, Natural Products, and Carbon Storage increased, while those for Tourism and Recreation, Coastal Protection, Sense of Place, Artisanal Fishing Opportunities, and Biodiversity experienced decline. Food Provision saw the most substantial growth, increasing by 16.44, while Tourism and Recreation experienced the steepest decline, dropping by 30.57. It was indicated that the quality of seawater had consistently remained at a high level, and the economy developed well. However, there was a slight decline in marine health, with a notable downturn in tourism. Coastal ecosystems still face ongoing risks of degradation. For the next steps, the focus should be on coastal zone protection and revitalizing the tourism industry. Greater support and effective management should be provided to traditional fisheries. We recommend that parameters and reference points be standardized, and emphasize the importance of prioritizing the use of official data for calculations and assessments when applying the OHI at the city-level to ensure comparability.","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"53 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction and analysis of China’s coastal marine economy: an innovative grey model with the best-matching data-preprocessing techniques","authors":"Zerong Wang, Zhijian Cai, Yao Li","doi":"10.3389/fmars.2025.1551352","DOIUrl":"https://doi.org/10.3389/fmars.2025.1551352","url":null,"abstract":"China’s coastal marine economy, a key part of the national economy, exhibits complex temporal evolution and regional heterogeneity, posing challenges for accurate forecasting. To address these challenges, this study employs advanced data-preprocessing techniques, accumulating generation operators (AGO) in grey prediction models, to tackle the nonlinear, volatile, and heterogeneous gross ocean product (GOP) data. Specifically, an accumulating generation operator matching mechanism that utilizes a pool of seven advanced AGOs is incorporated into the discrete grey prediction model. The proposed best-matching discrete grey prediction model can accurately describe the GOP system in China’s 11 coastal provinces. Furthermore, the experimental results indicate that the proposed model achieves 5.09% average forecasting mean absolute percentage error, demonstrating 46.65% and 61.73% improvement rates over the single AGO-based and benchmark models, respectively. Consequently, the proposed model is deployed to forecast China’s provincial GOP up to 2025, offering insights into the national development strategies, regionally tailored policies, and inter-provincial coordination in the marine sector.","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"36 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combating IUU fishing: an examination of interaction between China and regional fisheries management organizations","authors":"Shuo Li","doi":"10.3389/fmars.2025.1601534","DOIUrl":"https://doi.org/10.3389/fmars.2025.1601534","url":null,"abstract":"This paper examines China’s evolving engagement with Regional Fisheries Management Organizations (RFMOs) in addressing illegal, unreported, and unregulated (IUU) fishing. As the world’s largest fishing nation, China’s cooperation is crucial to achieving global fisheries sustainability. Through an analysis of legal instruments and case studies across eight RFMOs in which China participates, the study finds that China has progressively aligned its domestic regulations with RFMO measures—such as vessel licensing systems, observer programs, and IUU vessel blacklists. The incorporation of RFMO obligations into its national legislation, along with China’s cooperative approach toward RFMOs of which it is not a member, reflects a growing commitment to international fisheries governance. However, challenges remain. While China has actively engaged in RFMO decision-making processes, its cautious stance on certain issues highlights ongoing tensions both among member states and between states and international institutions. This study concludes that China’s regulatory reforms have enhanced its compliance and demonstrated its commitment to sustainable fisheries. However, further improvements in transparency and a more proactive role in international cooperation remain necessary. RFMOs provide valuable platforms for collaborative governance, and strengthening deeper and effective participation is essential to enhancing their overall function","PeriodicalId":12479,"journal":{"name":"Frontiers in Marine Science","volume":"134 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}