{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T10:55:56Z","timestamp":1776250556988,"version":"3.50.1"},"reference-count":66,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS\u20102026\u201025486262"],"award-info":[{"award-number":["RS\u20102026\u201025486262"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer Graphics Forum"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Diffusion models are powerful generative frameworks for producing high\u2010quality images by denoising latent variables from random noise. However, training with likelihood\u2010based objectives, such as denoising score matching, can lead to locally oversmoothed high\u2010frequency details, including fine textures and sharp edges, thereby limiting perceptual fidelity and structural detail. Adversarial training with GANs enhances sharpness but typically requires additional discriminator networks, increasing computational costs and destabilizing training. To this end, we propose Latent Diffusion Generative Adversarial Networks (LD\u2010GAN), a novel framework that seamlessly integrates adversarial learning into diffusion models without modifying their original pipeline. LD\u2010GAN leverages the pretrained variational autoencoder (VAE) in latent diffusion models as an energy\u2010based discriminator, enabling adversarial training without extra parameters and preserving the structured latent priors learned from large datasets. We also introduce a structural consistency energy that aligns encoder and decoder feature representations, thereby enhancing perceptual quality and compatibility with the pretrained latent space. Extensive experiments demonstrate that LD\u2010GAN significantly improves sample fidelity, perceptual sharpness, and diversity over state\u2010of\u2010the\u2010art baseline methods across various generation tasks while ensuring efficient training dynamics.<\/jats:p>","DOI":"10.1111\/cgf.70409","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T10:07:31Z","timestamp":1776247651000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Latent Diffusion\u2010GAN: Adversarial Learning in the Autoencoded Latent Space"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8742-3398","authenticated-orcid":false,"given":"U\u2010Chae","family":"Jun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9914-481X","authenticated-orcid":false,"given":"Jaeeun","family":"Ko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7622-0817","authenticated-orcid":false,"given":"Jiwoo","family":"Kang","sequence":"additional","affiliation":[{"name":"Sookmyung Women's University  South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"e_1_2_7_2_2","unstructured":"Balaji Yogesh Nah Seungjun Huang Xun et al. \u201cEDIFF-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers\u201d.arXiv preprint arXiv:2211.01324(2022) 8."},{"key":"e_1_2_7_3_2","doi-asserted-by":"crossref","unstructured":"Deng Jia Dong Wei Socher Richard et al. \u201cImageNet: A Large-Scale Hierarchical Image Database\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).2009 248\u201325519 20.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_7_4_2","unstructured":"Downs Laura Francis Anthony Koenig Nate et al. \u201cGoogle Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items\u201d.Proceedings of the International Conference on Robotics and Automation (ICRA).202212."},{"key":"e_1_2_7_5_2","unstructured":"Du YilunandMordatch Igor. \u201cImplicit Generation and Modeling with Energy-Based Models\u201d.Proceedings of the Neural Information Processing Systems (NeurIPS).20192 3 5."},{"key":"e_1_2_7_6_2","unstructured":"Dhariwal PrafullaandNichol Alex. \u201cDiffusion models beat GANs on image synthesis\u201d.Proceedings of the Advances in Neural Information Processing Systems (NeurIPS).2021 8780\u201387941\u20133."},{"key":"e_1_2_7_7_2","unstructured":"Deitke Matt Schwenk Dustin Salvador Jordi et al. \u201cObjaverse: A Universe of Annotated 3D Objects\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).202311."},{"key":"e_1_2_7_8_2","unstructured":"Gal Rinon Alaluf Yuval Atzmon Yuval et al. \u201cAn Image Is Worth One Word: Personalizing Text-to-Image Generation Using Textual Inversion\u201d.arXiv preprint arXiv:2208.01618(2022) 6\u201310."},{"key":"e_1_2_7_9_2","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow Ian","year":"2014","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"issue":"4","key":"e_1_2_7_10_2","first-page":"1","article-title":"StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators","volume":"41","author":"Gal Rinon","year":"2022","journal-title":"ACM Transactions on Graphics (ToG)"},{"key":"e_1_2_7_11_2","unstructured":"Grathwohl Will Wang Kuan-Chieh Jacobsen Joern-Henrik et al. \u201cYour classifier is secretly an energy based model and you should treat it like one\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20207."},{"key":"e_1_2_7_12_2","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho Jonathan","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"e_1_2_7_13_2","first-page":"1225","volume-title":"Proceedings of the International Conference on Machine Learning (ICML)","author":"Hardt Moritz","year":"2016"},{"key":"e_1_2_7_14_2","unstructured":"Heusel Martin Ramsauer Hubert Unterthiner Thomas et al. \u201cGANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium\u201d.Proceedings of the Advances in Neural Information Processing Systems (NeurIPS).2017 6626\u201366378 9 13 20."},{"key":"e_1_2_7_15_2","unstructured":"Jun U Ko Jaeeun Kang Jiwoo et al. \u201cGenerative adversarial diffusion\u201d.Proceedings of the IEEE\/CVF International Conference on Computer Vision.2025 16786\u2013167961."},{"key":"e_1_2_7_16_2","unstructured":"Karras Tero Aittala Miika Aila Timo andLaine Samuli. \u201cElucidating the design space of diffusion-based generative models\u201d.Proceedings of the Advances in Neural Information Processing Systems (NeurIPS).20222."},{"key":"e_1_2_7_17_2","doi-asserted-by":"crossref","unstructured":"Karras Tero Laine Samuli andAila Timo. \u201cA style-based generator architecture for generative adversarial networks\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).2019 4401\u2013441020.","DOI":"10.1109\/CVPR.2019.00453"},{"issue":"5","key":"e_1_2_7_18_2","doi-asserted-by":"crossref","first-page":"2858","DOI":"10.1109\/TSMC.2021.3054677","article-title":"Competitive learning of facial fitting and synthesis using UV energy","volume":"52","author":"Kang Jiwoo","year":"2021","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"e_1_2_7_19_2","unstructured":"Kingma Diederik PandWelling Max. \u201cAuto-encoding variational bayes\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20143."},{"key":"e_1_2_7_20_2","doi-asserted-by":"crossref","unstructured":"Kang Minguk Zhang Richard Barnes Connelly et al. \u201cDistilling diffusion models into conditional GANs\u201d.Proceedings of the European Conference on Computer Vision (ECCV).2024 428\u20134472 3.","DOI":"10.1007\/978-3-031-73390-1_25"},{"key":"e_1_2_7_21_2","volume-title":"Theory of Point Estimation","author":"Lehmann Erich L.","year":"1998"},{"key":"e_1_2_7_22_2","doi-asserted-by":"crossref","unstructured":"Long Xiaoxiao Guo Yuan-Chen Lin Cheng et al. \u201cWonder3D: Single Image to 3D Using Cross-Domain Diffusion\u201d.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2024 9970\u2013998011.","DOI":"10.1109\/CVPR52733.2024.00951"},{"key":"e_1_2_7_23_2","first-page":"55975","article-title":"Era3D: HighResolution Multiview Diffusion Using Efficient Row-Wise Attention","volume":"37","author":"Li Peng","year":"2024","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"e_1_2_7_24_2","doi-asserted-by":"crossref","unstructured":"Li Yifan Liu Huan Wu Qian et al. \u201cGLIGEN: Open-Set Grounded Text-to-Image Generation\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).2023 22511\u2013225216\u20139.","DOI":"10.1109\/CVPR52729.2023.02156"},{"key":"e_1_2_7_25_2","doi-asserted-by":"crossref","unstructured":"Liu Ziwei Luo Ping Wang Xiaogang andTang Xiaoou. \u201cDeep learning face attributes in the wild\u201d.Proceedings of the IEEE International Conference on Computer Vision (ICCV).2015 3730\u2013373820.","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_2_7_26_2","unstructured":"Liu Yuan Lin Cheng Zeng Zijiao et al. \u201cSyncDreamer: Generating Multiview-Consistent Images from a Single-View Image\u201d.arXiv preprint arXiv:2309.03453(2023) 6\u20138 11 12."},{"key":"e_1_2_7_27_2","doi-asserted-by":"crossref","unstructured":"Lin Tsung-Yi Maire Michael Belongie Serge et al. \u201cMicrosoft COCO: Common Objects in Context\u201d.Proceedings of the European Conference on Computer Vision (ECCV).2014 740\u20137558 13 14 18 19.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_2_7_28_2","doi-asserted-by":"crossref","unstructured":"Liu Ruoshi Wu Rundi Van Hoorick Basile et al. \u201cZero-1-to-3: Zero-Shot One Image to 3D Object\u201d.Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV).2023 9298\u2013930911.","DOI":"10.1109\/ICCV51070.2023.00853"},{"key":"e_1_2_7_29_2","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1007\/s11633-025-1562-4","article-title":"DPM-Solver++: Fast solver for guided sampling of diffusion probabilistic models","volume":"22","author":"Lu Cheng","year":"2025","journal-title":"Machine Intelligence Research"},{"key":"e_1_2_7_30_2","doi-asserted-by":"crossref","unstructured":"Li Liunian Harold Zhang Pengchuan Zhang Haotian et al. \u201cGrounded Language-Image Pre-Training\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).2022 10965\u2013109759.","DOI":"10.1109\/CVPR52688.2022.01069"},{"key":"e_1_2_7_31_2","doi-asserted-by":"crossref","unstructured":"Li Liunian Harold Zhang Pengchuan Zhang Haotian et al. \u201cGrounded Language-Image Pre-Training\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).2022 10965\u2013109759.","DOI":"10.1109\/CVPR52688.2022.01069"},{"key":"e_1_2_7_32_2","volume-title":"Proceedings of the International Conference on Machine Learning (ICML)","author":"Nichol Alexander Quinn","year":"2021"},{"key":"e_1_2_7_33_2","volume-title":"Proceedings of the International Conference on Machine Learning (ICML)","author":"Nie Weili","year":"2022"},{"key":"e_1_2_7_34_2","unstructured":"Ordonez Vicente Kulkarni Girish andBerg Tamara L.\u201cIm2Text: Describing Images Using 1 Million Captioned Photographs\u201d.Proceedings of the Advances in Neural Information Processing Systems (NeurIPS).20119."},{"key":"e_1_2_7_35_2","unstructured":"Podell Dustin English Zion Lacey Kyle et al. \u201cSDXL: Improving Latent Diffusion Models for High-resolution Image Synthesis\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20248."},{"key":"e_1_2_7_36_2","first-page":"21994","article-title":"Learning latent space energy-based prior model","volume":"33","author":"Pang Bo","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"e_1_2_7_37_2","first-page":"6","volume":"2","author":"Rombach Robin","year":"2022","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_2_7_38_2","first-page":"8748","volume-title":"Proceedings of the International Conference on Machine Learning (ICML)","author":"Radford Alec","year":"2021"},{"key":"e_1_2_7_39_2","first-page":"18","article-title":"LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models","volume":"35","author":"Schuhmann Christoph","year":"2022","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"e_1_2_7_40_2","first-page":"11918","article-title":"Generative modeling by estimating gradients of the data distribution","volume":"32","author":"Song Yang","year":"2019","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"e_1_2_7_41_2","unstructured":"Salimans Tim Goodfellow Ian Zaremba Wojciech et al. \u201cImproved techniques for training GANs\u201d.Proceedings of the Neural Information Processing Systems (NeurIPS).2016 2234\u201322425."},{"key":"e_1_2_7_42_2","unstructured":"Salimans Tim Ho Jonathan Chen Xi andSohlDickstein Jascha. \u201cProgressive distillation for fast sampling of diffusion models\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20221 2."},{"key":"e_1_2_7_43_2","doi-asserted-by":"crossref","unstructured":"Shao Shuang Li Zhi Zhang Tianlong et al. \u201cObjects365: A Large-Scale High-Quality Dataset for Object Detection\u201d.Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV).2019 8430\u201384399.","DOI":"10.1109\/ICCV.2019.00852"},{"key":"e_1_2_7_44_2","unstructured":"Song Jiaming Meng Chenlin andErmon Stefano. \u201cDenoising diffusion implicit models\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20212 3."},{"key":"e_1_2_7_45_2","unstructured":"Singer Uriel Polyak Adam Hayes Thomas et al. \u201cMake-a-video: Text-to-video generation without text-video data\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20231."},{"key":"e_1_2_7_46_2","unstructured":"Sehwag Vikash Wang Shiqi Chiang Mung andMittal Prateek. \u201cSSD: A Unified Framework for Self-Supervised Outlier Detection\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20212 18 20."},{"key":"e_1_2_7_47_2","first-page":"2256","volume-title":"Proceedings of the International Conference on Machine Learning (ICML)","author":"Sohl-Dickstein Jascha","year":"2015"},{"key":"e_1_2_7_48_2","unstructured":"Simonyan KarenandZisserman Andrew. \u201cVery Deep Convolutional Networks for Large-Scale Image Recognition\u201d.Proceedings of the International Conference on Learning Representations (ICLR).201514."},{"key":"e_1_2_7_49_2","doi-asserted-by":"crossref","first-page":"78161","DOI":"10.1109\/ACCESS.2024.3406535","article-title":"Latent de-noising diffusion gan: Faster sampling, higher image quality","volume":"12","author":"Trinh Luan Thanh","year":"2024","journal-title":"IEEE Access"},{"key":"e_1_2_7_50_2","unstructured":"Tack Jihoon Mo Sangwoo Jeong Jongheon andShin Jinwoo. \u201cCSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances\u201d.Proceedings of the Neural Information Processing Systems (NeurIPS).20202."},{"key":"e_1_2_7_51_2","unstructured":"Ulyanov Dmitry Vedaldi Andrea andLempitsky Victor. \u201cInstance normalization: The missing ingredient for fast stylization\u201d.arXiv preprint arXiv:1607.08022(2016) 4 13."},{"key":"e_1_2_7_52_2","first-page":"11287","article-title":"Score-based generative modeling in latent space","volume":"34","author":"Vahdat Arash","year":"2021","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"issue":"4","key":"e_1_2_7_53_2","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang Zhou","year":"2004","journal-title":"IEEE Transactions on Image Processing (TIP)"},{"key":"e_1_2_7_54_2","unstructured":"Watson Daniel Chan William Ho Jonathan andNorouzi Mohammad. \u201cLearning fast samplers for diffusion models by differentiating through sample quality\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20223."},{"key":"e_1_2_7_55_2","doi-asserted-by":"crossref","unstructured":"Wu YuxinandHe Kaiming. \u201cGroup normalization\u201d.Proceedings of the European Conference on Computer Vision (ECCV).2018 3\u20131913.","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"e_1_2_7_56_2","volume-title":"Proceedings of the International Conference on Machine Learning (ICML)","author":"Wang Zekai","year":"2023"},{"key":"e_1_2_7_57_2","unstructured":"Wu Tong Zhang Jiarui Fu Xiao et al. \u201cOmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception Reconstruction and Generation\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).20239 12."},{"key":"e_1_2_7_58_2","unstructured":"Wang Zhendong Zheng Huangjie He Pengcheng et al. \u201cDiffusion-GAN: Training GANs with diffusion\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20231\u20133 19 20."},{"key":"e_1_2_7_59_2","unstructured":"Xiao Zhisheng Kreis Karsten andVahdat Arash. \u201cTackling the Generative Learning Trilemma with Denoising Diffusion GANs\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20222."},{"key":"e_1_2_7_60_2","doi-asserted-by":"crossref","first-page":"10441","DOI":"10.1609\/aaai.v35i12.17250","article-title":"Learning energy-based model with variational auto-encoder as amortized sampler","volume":"35","author":"Xie Jianwen","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)."},{"key":"e_1_2_7_61_2","doi-asserted-by":"crossref","unstructured":"Xu Yanwu Zhao Yang Xiao Zhisheng andHou Tingbo. \u201cUFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs\u201d.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2024 8196\u201382062.","DOI":"10.1109\/CVPR52733.2024.00783"},{"key":"e_1_2_7_62_2","doi-asserted-by":"crossref","unstructured":"Yin Tianwei Gharbi Micha\u00ebl Zhang Richard et al. \u201cOne-step diffusion with distribution matching distillation\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).2024 6613\u201366232 3.","DOI":"10.1109\/CVPR52733.2024.00632"},{"key":"e_1_2_7_63_2","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1162\/tacl_a_00166","article-title":"From Image Descriptions to Visual Denotations: New Similarity Metrics for Semantic Inference over Event Descriptions","volume":"2","author":"Young Peter","year":"2014","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"e_1_2_7_64_2","unstructured":"Yu Fisher Seff Ari Zhang Yinda et al. \u201cLSUN: Construction of a large-scale image dataset using deep learning with humans in the loop\u201d.arXiv preprint arXiv:1506.03365(2015) 20."},{"key":"e_1_2_7_65_2","unstructured":"Zhang Richard Isola Phillip Efros Alexei A. et al. \u201cThe Unreasonable Effectiveness of Deep Features as a Perceptual Metric\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).20182 3 5 6 11 14."},{"key":"e_1_2_7_66_2","unstructured":"Zhao Junbo Mathieu Michael andLeCun Yann. \u201cEnergy-based generative adversarial network\u201d.Proceedings of the International Conference on Learning Representations (ICLR).20171\u20134 6 7."},{"key":"e_1_2_7_67_2","unstructured":"Zhang Lvmin Rao Anyi andAgrawala Maneesh. \u201cAdding conditional control to text-to-image diffusion models\u201d.Proceedings of the IEEE International Conference on Computer Vision (ICCV).20238 20."}],"container-title":["Computer Graphics Forum"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/cgf.70409","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/cgf.70409","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/cgf.70409","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T10:08:06Z","timestamp":1776247686000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/cgf.70409"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,15]]},"references-count":66,"alternative-id":["10.1111\/cgf.70409"],"URL":"https:\/\/doi.org\/10.1111\/cgf.70409","archive":["Portico"],"relation":{},"ISSN":["0167-7055","1467-8659"],"issn-type":[{"value":"0167-7055","type":"print"},{"value":"1467-8659","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,15]]},"assertion":[{"value":"2026-04-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70409"}}