{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:14:41Z","timestamp":1771467281551,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T00:00:00Z","timestamp":1707350400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T00:00:00Z","timestamp":1707350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004955","name":"\u00d6sterreichische Forschungsf\u00f6rderungsgesellschaft","doi-asserted-by":"publisher","award":["886385"],"award-info":[{"award-number":["886385"]}],"id":[{"id":"10.13039\/501100004955","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004955","name":"\u00d6sterreichische Forschungsf\u00f6rderungsgesellschaft","doi-asserted-by":"publisher","award":["FO999892420"],"award-info":[{"award-number":["FO999892420"]}],"id":[{"id":"10.13039\/501100004955","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10845-023-02313-y","type":"journal-article","created":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T19:02:41Z","timestamp":1707418961000},"page":"1491-1503","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluation of data augmentation and loss functions in semantic image segmentation for drilling tool wear detection"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8880-6283","authenticated-orcid":false,"given":"Elke","family":"Schlager","sequence":"first","affiliation":[]},{"given":"Andreas","family":"Windisch","sequence":"additional","affiliation":[]},{"given":"Lukas","family":"Hanna","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Kl\u00fcnsner","sequence":"additional","affiliation":[]},{"given":"Elias Jan","family":"Hagendorfer","sequence":"additional","affiliation":[]},{"given":"Tamara","family":"Feil","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,8]]},"reference":[{"key":"2313_CR1","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., ..., & Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. https:\/\/www.tensorflow.org\/. Software available from tensorflow.org."},{"key":"2313_CR2","doi-asserted-by":"crossref","unstructured":"Abulnaga, S. M., & Rubin, J. (2019). Ischemic stroke lesion segmentation in CT perfusion scans using pyramid pooling and focal loss. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4, pp. 352\u2013363. Springer.","DOI":"10.1007\/978-3-030-11723-8_36"},{"issue":"12","key":"2313_CR3","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481\u20132495. https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2313_CR4","doi-asserted-by":"publisher","first-page":"6531","DOI":"10.1109\/JSTARS.2022.3197937","volume":"15","author":"H Bai","year":"2022","unstructured":"Bai, H., Cheng, J., Su, Y., Liu, S., & Liu, X. (2022). Calibrated focal loss for semantic labeling of high-resolution remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 6531\u20136547. https:\/\/doi.org\/10.1109\/JSTARS.2022.3197937","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"2313_CR5","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1016\/j.promfg.2020.05.134","volume":"48","author":"T Bergs","year":"2020","unstructured":"Bergs, T., Holst, C., Gupta, P., & Augspurger, T. (2020). Digital image processing with deep learning for automated cutting tool wear detection. Procedia Manufacturing, 48, 947\u2013958. https:\/\/doi.org\/10.1016\/j.promfg.2020.05.134","journal-title":"Procedia Manufacturing"},{"issue":"4","key":"2313_CR6","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834\u2013848. https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"2313_CR7","doi-asserted-by":"publisher","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017b). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587. https:\/\/doi.org\/10.48550\/arXiv.1706.05587","DOI":"10.48550\/arXiv.1706.05587"},{"key":"2313_CR8","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pp. 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2313_CR9","unstructured":"Chollet, F., et al. (2015a). Keras. https:\/\/github.com\/fchollet\/keras"},{"key":"2313_CR10","unstructured":"Chollet, F., et al. (2015b). Keras binary cross entropy. https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/keras\/losses\/BinaryCrossentropy"},{"key":"2313_CR11","unstructured":"Chollet, F., et al. (2015c). Keras binary focal cross entropy. https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/keras\/losses\/BinaryFocalCrossentropy"},{"key":"2313_CR12","unstructured":"Chollet, F., et al. (2015d). Keras categorical cross entropy. https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/keras\/losses\/CategoricalCrossentropy"},{"key":"2313_CR13","unstructured":"Chollet, F., et al. (2015e). Keras sparse categorical focal loss. https:\/\/focal-loss.readthedocs.io\/en\/latest\/generated\/focal_loss.SparseCategoricalFocalLoss.html"},{"key":"2313_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/machines9120351","author":"L Colantonio","year":"2021","unstructured":"Colantonio, L., Equeter, L., Dehombreux, P., & Ducobu, F. (2021). A systematic literature review of cutting tool wear monitoring in turning by using artificial intelligence techniques. Machines. https:\/\/doi.org\/10.3390\/machines9120351","journal-title":"Machines"},{"key":"2313_CR15","doi-asserted-by":"publisher","unstructured":"Doi, K., & Iwasaki, A. (2018). The effect of focal loss in semantic segmentation of high resolution aerial image. In IGARSS 2018\u20132018 IEEE international geoscience and remote sensing symposium, pp. 6919\u20136922. IEEE. https:\/\/doi.org\/10.1109\/IGARSS.2018.8519409","DOI":"10.1109\/IGARSS.2018.8519409"},{"issue":"11","key":"2313_CR16","doi-asserted-by":"publisher","first-page":"3679","DOI":"10.1109\/TMI.2020.3002417","volume":"39","author":"T Eelbode","year":"2020","unstructured":"Eelbode, T., Bertels, J., Berman, M., Vandermeulen, D., Maes, F., Bisschops, R., & Blaschko, M. B. (2020). Optimization for medical image segmentation: Theory and practice when evaluating with dice score or Jaccard index. IEEE Transactions on Medical Imaging, 39(11), 3679\u20133690. https:\/\/doi.org\/10.1109\/TMI.2020.3002417","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2313_CR17","unstructured":"Gubins, I. (2022). Tiler. https:\/\/github.com\/the-lay\/tiler"},{"issue":"2","key":"2313_CR18","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1016\/j.ifacol.2022.04.249","volume":"55","author":"C Holst","year":"2022","unstructured":"Holst, C., Yavuz, T. B., Gupta, P., Ganser, P., & Bergs, T. (2022). Deep learning and rule-based image processing pipeline for automated metal cutting tool wear detection and measurement. IFAC-Papers OnLine, 55(2), 534\u2013539. https:\/\/doi.org\/10.1016\/j.ifacol.2022.04.249","journal-title":"IFAC-Papers OnLine"},{"key":"2313_CR19","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2021.07.055","volume":"462","author":"MS Hossain","year":"2021","unstructured":"Hossain, M. S., Betts, J. M., & Paplinski, A. P. (2021). Dual focal loss to address class imbalance in semantic segmentation. Neurocomputing, 462, 69\u201387. https:\/\/doi.org\/10.1016\/j.neucom.2021.07.055","journal-title":"Neurocomputing"},{"key":"2313_CR20","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., & Wu, J. (2020). Unet 3+: A full-scale connected unet for medical image segmentation. In ICASSP 2020\u20132020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 1055\u20131059. IEEE.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"2313_CR21","doi-asserted-by":"crossref","unstructured":"Jadon, S. (2020). A survey of loss functions for semantic segmentation. In 2020 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB), pp. 1\u20137. IEEE.","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"key":"2313_CR22","doi-asserted-by":"publisher","unstructured":"Karen, S., & Andrew, Z. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint. https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"2313_CR23","doi-asserted-by":"publisher","first-page":"1326","DOI":"10.1016\/j.proeng.2014.03.125","volume":"69","author":"M Klaic","year":"2014","unstructured":"Klaic, M., Staroveski, T., & Udiljak, T. (2014). Tool wear classification using decision trees in stone drilling applications: A preliminary study. Procedia Engineering, 69, 1326\u20131335. https:\/\/doi.org\/10.1016\/j.proeng.2014.03.125","journal-title":"Procedia Engineering"},{"key":"2313_CR24","doi-asserted-by":"publisher","first-page":"571","DOI":"10.2507\/IJSIMM14(4)1.301","volume":"14","author":"S Klancnik","year":"2015","unstructured":"Klancnik, S., Ficko, M., Balic, J., & Pahole, I. (2015). Computer vision-based approach to end mill tool monitoring. International Journal of Simulation Modelling, 14, 571\u2013583. https:\/\/doi.org\/10.2507\/IJSIMM14(4)1.301","journal-title":"International Journal of Simulation Modelling"},{"key":"2313_CR25","doi-asserted-by":"publisher","unstructured":"Kolarik, M., Burget, R., & Riha, K. (2020). Comparing normalization methods for limited batch size segmentation neural networks. In 2020 43rd international conference on telecommunications and signal processing (TSP), pp. 677\u2013680. IEEE. https:\/\/doi.org\/10.1109\/TSP49548.2020.9163397","DOI":"10.1109\/TSP49548.2020.9163397"},{"key":"2313_CR26","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R. B., He, K., & Doll\u00e1r, P. (2017). Focal loss for dense object detection. CoRR. arXiv:1708.02002","DOI":"10.1109\/ICCV.2017.324"},{"key":"2313_CR27","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-97610-y","author":"W-J Lin","year":"2021","unstructured":"Lin, W.-J., Chen, J.-W., Jhuang, J.-P., Tsai, M.-S., Hung, C.-L., Li, K.-M., & Young, H.-T. (2021). Publisher correction: Integrating object detection and image segmentation for detecting the tool wear area on stitched image. Scientific Reports. https:\/\/doi.org\/10.1038\/s41598-021-97610-y","journal-title":"Scientific Reports"},{"key":"2313_CR28","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"2313_CR29","doi-asserted-by":"publisher","unstructured":"Lutz, B., Kisskalt, D., Regulin, D., Reisch, R., Schiffler, A., & Franke, J. (2019). Evaluation of deep learning for semantic image segmentation in tool condition monitoring. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pages 2008\u20132013. https:\/\/doi.org\/10.1109\/ICMLA.2019.00321.","DOI":"10.1109\/ICMLA.2019.00321"},{"key":"2313_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00170-019-04090-6","volume":"104","author":"G Martinez-Arellano","year":"2019","unstructured":"Martinez-Arellano, G., Terrazas, G., & Ratchev, S. (2019). Tool wear classification using time series imaging and deep learning. The International Journal of Advanced Manufacturing Technology, 104, 1.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2313_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.3033457","volume":"70","author":"H Miao","year":"2021","unstructured":"Miao, H., Zhao, Z., Sun, C., Li, B., & Yan, R. (2021). A u-net-based approach for tool wear area detection and identification. IEEE Transactions on Instrumentation and Measurement, 70, 1\u201310. https:\/\/doi.org\/10.1109\/TIM.2020.3033457","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2313_CR32","doi-asserted-by":"publisher","DOI":"10.3390\/sym9120296","author":"OG Moldovan","year":"2017","unstructured":"Moldovan, O. G., Dzitac, S., Moga, I., Vesselenyi, T., & Dzitac, I. (2017). Tool-wear analysis using image processing of the tool flank. Symmetry. https:\/\/doi.org\/10.3390\/sym9120296","journal-title":"Symmetry"},{"key":"2313_CR33","doi-asserted-by":"publisher","DOI":"10.1186\/s13104-022-06096-y","author":"D M\u00fcller","year":"2022","unstructured":"M\u00fcller, D., Soto-Rey, I., & Kramer, F. (2022). Towards a guideline for evaluation metrics in medical image segmentation. BMC Research Notes. https:\/\/doi.org\/10.1186\/s13104-022-06096-y","journal-title":"BMC Research Notes"},{"key":"2313_CR34","doi-asserted-by":"publisher","DOI":"10.3390\/app12168100","author":"L Qin","year":"2022","unstructured":"Qin, L., Zhou, X., & Wu, X. (2022). Research on wear detection of end milling cutter edge based on image stitching. Applied Sciences. https:\/\/doi.org\/10.3390\/app12168100","journal-title":"Applied Sciences"},{"key":"2313_CR35","doi-asserted-by":"publisher","unstructured":"Rahman, M. A., & Wang, Y. (2016). Optimizing intersection-over-union in deep neural networks for image segmentation. In International symposium on visual computing, pp. 234\u2013244. Springer. https:\/\/doi.org\/10.1007\/978-3-319-50835-1_22","DOI":"10.1007\/978-3-319-50835-1_22"},{"key":"2313_CR36","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International conference on medical image computing and computer-assisted intervention, pp. 234\u2013241. Springer. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"2313_CR37","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.jmatprotec.2005.04.072","volume":"170","author":"C Sanjay","year":"2005","unstructured":"Sanjay, C., Neema, M., & Chin, C. (2005). Modeling of tool wear in drilling by statistical analysis and artificial neural network. Journal of Materials Processing Technology, 170(3), 494\u2013500. https:\/\/doi.org\/10.1016\/j.jmatprotec.2005.04.072","journal-title":"Journal of Materials Processing Technology"},{"key":"2313_CR38","unstructured":"Schlager, E. (2022). Unet-drilling. https:\/\/github.com\/eschlager\/UNet-Drilling"},{"issue":"21","key":"2313_CR39","doi-asserted-by":"publisher","first-page":"30519","DOI":"10.1007\/s11042-022-12821-3","volume":"81","author":"U Sehar","year":"2022","unstructured":"Sehar, U., & Naseem, M. L. (2022). How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison. Multimedia Tools and Applications, 81(21), 30519\u201330544. https:\/\/doi.org\/10.1007\/s11042-022-12821-3","journal-title":"Multimedia Tools and Applications"},{"key":"2313_CR40","doi-asserted-by":"publisher","unstructured":"Shurrab, S., Almshnanah, A., & Duwairi, R. (2021). Tool wear prediction in computer numerical control milling operations via machine learning. In 2021 12th international conference on information and communication systems (ICICS), pp. 220\u2013227. https:\/\/doi.org\/10.1109\/ICICS52457.2021.9464580","DOI":"10.1109\/ICICS52457.2021.9464580"},{"key":"2313_CR41","doi-asserted-by":"publisher","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","volume":"9","author":"N Siddique","year":"2021","unstructured":"Siddique, N., Paheding, S., Elkin, C. P., & Devabhaktuni, V. (2021). U-net and its variants for medical image segmentation: A review of theory and applications. IEEE Access, 9, 82031\u201382057. https:\/\/doi.org\/10.1109\/ACCESS.2021.3086020","journal-title":"IEEE Access"},{"key":"2313_CR42","doi-asserted-by":"publisher","unstructured":"Summers, C., & Dinneen, M. J. (2019). Four things everyone should know to improve batch normalization. In International conference on learning representations. https:\/\/doi.org\/10.48550\/arXiv.1906.03548","DOI":"10.48550\/arXiv.1906.03548"},{"key":"2313_CR43","doi-asserted-by":"publisher","unstructured":"van Beers, F., Lindstr\u00f6m, A., Okafor, E., & Wiering, M. (2019). Deep neural networks with intersection over union loss for binary image segmentation. In Proceedings of the 8th international conference on pattern recognition applications and methods, pp. 438\u2013445. SciTePress. https:\/\/doi.org\/10.5220\/0007347504380445","DOI":"10.5220\/0007347504380445"},{"key":"2313_CR44","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"1999","unstructured":"Vapnik, V. (1999). The nature of statistical learning theory. Springer."},{"issue":"1115\/1","key":"2313_CR45","first-page":"4036350","volume":"10","author":"D Wu","year":"2017","unstructured":"Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A comparative study on machine learning algorithms for smart manufacturing: Tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, 10(1115\/1), 4036350.","journal-title":"Journal of Manufacturing Science and Engineering"},{"key":"2313_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/s11740-012-0395-5","author":"J Zhang","year":"2012","unstructured":"Zhang, J., Zhang, C., Guo, S., & Zhou, L. (2012). Research on tool wear detection based on machine vision in end milling process. Production Engineering. https:\/\/doi.org\/10.1007\/s11740-012-0395-5","journal-title":"Production Engineering"},{"key":"2313_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881\u20132890.","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02313-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02313-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02313-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T22:29:29Z","timestamp":1738621769000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02313-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,8]]},"references-count":47,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2313"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02313-y","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,8]]},"assertion":[{"value":"10 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}