{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:13:54Z","timestamp":1778084034509,"version":"3.51.4"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Ministry of Economic Development of the Russian Federation","award":["000000C313925P4E0002"],"award-info":[{"award-number":["000000C313925P4E0002"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s10994-026-07000-6","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T12:52:54Z","timestamp":1772801574000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Online Neural Networks for Change-Point Detection"],"prefix":"10.1007","volume":"115","author":[{"given":"Mikhail","family":"Hushchyn","sequence":"first","affiliation":[]},{"given":"Kenenbek","family":"Arzymatov","sequence":"additional","affiliation":[]},{"given":"Denis","family":"Derkach","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"7000_CR1","doi-asserted-by":"crossref","unstructured":"Ahad, N., & Davenport, M.A. (2021). Semi-supervised sequence classification through change point detection. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35, pp. 6574\u20136581","DOI":"10.1609\/aaai.v35i8.16814"},{"issue":"2","key":"7000_CR2","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s10115-016-0987-z","volume":"51","author":"S Aminikhanghahi","year":"2017","unstructured":"Aminikhanghahi, S., & Cook, D. J. (2017). A survey of methods for time series change point detection. Knowledge and Information Systems, 51(2), 339\u2013367. https:\/\/doi.org\/10.1007\/s10115-016-0987-z","journal-title":"Knowledge and Information Systems"},{"issue":"3","key":"7000_CR3","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1017\/S0266466600005831","volume":"13","author":"J Bai","year":"1997","unstructured":"Bai, J. (1997). Estimating multiple breaks one at a time. Econometric Theory, 13(3), 315\u2013352.","journal-title":"Econometric Theory"},{"key":"7000_CR4","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5308","author":"P Baldi","year":"2014","unstructured":"Baldi, P., Sadowski, P., & Whiteson, D. (2014). Searching for exotic particles in high-energy physics with deep learning. Nature Communications. https:\/\/doi.org\/10.1038\/ncomms5308","journal-title":"Nature Communications"},{"key":"7000_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108217","volume":"133","author":"X Bao","year":"2024","unstructured":"Bao, X., Chen, L., Zhong, J., Wu, D., & Zheng, Y. (2024). A self-supervised contrastive change point detection method for industrial time series. Engineering Applications of Artificial Intelligence, 133, Article 108217.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"7000_CR6","volume-title":"Detection of abrupt changes: Theory and application","author":"M Basseville","year":"1993","unstructured":"Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application. Prentice-Hall Inc."},{"key":"7000_CR7","doi-asserted-by":"crossref","unstructured":"Bazarova, A., Romanenkova, E., & Zaytsev, A. (2024). Normalizing self-supervised learning for provably reliable change point detection. In: 2024 IEEE International conference on data mining (ICDM), pp. 21\u201330 . IEEE","DOI":"10.1109\/ICDM59182.2024.00009"},{"key":"7000_CR8","unstructured":"Burg, G.J.J., & Williams, C.K.I. (2020). An evaluation of change point detection algorithms"},{"key":"7000_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/S0962492900002804","volume":"7","author":"RE Caflisch","year":"1998","unstructured":"Caflisch, R. E. (1998). Monte carlo and quasi-monte carlo methods. Acta Numerica, 7, 1\u201349. https:\/\/doi.org\/10.1017\/S0962492900002804","journal-title":"Acta Numerica"},{"key":"7000_CR10","unstructured":"Chang, W.-C., Li, C.-L., Yang, Y., & P\u00f3czos, B. (2019). Kernel change-point detection with auxiliary deep generative models. arXiv preprint arXiv:1901.06077"},{"key":"7000_CR11","unstructured":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In: International conference on machine learning, pp. 1597\u20131607 . PmLR"},{"key":"7000_CR12","doi-asserted-by":"crossref","unstructured":"Chopra, S., Hadsell, R., & LeCun, Y. (2005). Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), vol. 1, pp. 539\u2013546. IEEE","DOI":"10.1109\/CVPR.2005.202"},{"key":"7000_CR13","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.1109\/TSP.2021.3087031","volume":"69","author":"T De Ryck","year":"2021","unstructured":"De Ryck, T., De Vos, M., & Bertrand, A. (2021). Change point detection in time series data using autoencoders with a time-invariant representation. IEEE Transactions on Signal Processing, 69, 3513\u20133524.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"7000_CR14","doi-asserted-by":"crossref","unstructured":"Deldari, S., Smith, D.V., Xue, H., & Salim, F.D. (2021). Time series change point detection with self-supervised contrastive predictive coding. In: Proceedings of the web conference 2021, pp. 3124\u20133135","DOI":"10.1145\/3442381.3449903"},{"key":"7000_CR15","unstructured":"Dua, D., & Graff, C. (2017). UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"issue":"12","key":"7000_CR16","doi-asserted-by":"publisher","first-page":"15604","DOI":"10.1109\/TPAMI.2023.3308189","volume":"45","author":"E Eldele","year":"2023","unstructured":"Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C.-K., Li, X., & Guan, C. (2023). Self-supervised contrastive representation learning for semi-supervised time-series classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12), 15604\u201315618.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"6","key":"7000_CR17","doi-asserted-by":"publisher","first-page":"3326","DOI":"10.3390\/s23063326","volume":"23","author":"Y Fang","year":"2023","unstructured":"Fang, Y., Zhai, Q., Zhang, Z., & Yang, J. (2023). Change point detection for fine-grained mfr work modes with multi-head attention-based bi-lstm network. Sensors, 23(6), 3326.","journal-title":"Sensors"},{"key":"7000_CR18","unstructured":"Franceschi, J.-Y., Dieuleveut, A., & Jaggi, M. (2019). Unsupervised scalable representation learning for multivariate time series. Advances in Neural Information Processing Systems 32"},{"issue":"6","key":"7000_CR19","doi-asserted-by":"publisher","first-page":"2243","DOI":"10.1214\/14-AOS1245","volume":"42","author":"P Fryzlewicz","year":"2014","unstructured":"Fryzlewicz, P. (2014). Wild binary segmentation for multiple change-point detection. Ann. Statist., 42(6), 2243\u20132281. https:\/\/doi.org\/10.1214\/14-AOS1245","journal-title":"Ann. Statist."},{"key":"7000_CR20","unstructured":"Fukushima, S., Nitanda, A., & Yamanishi, K. (2020). Online robust and adaptive learning from data streams. arXiv preprint arXiv:2007.12160"},{"key":"7000_CR21","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks"},{"key":"7000_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123342","volume":"248","author":"M Gupta","year":"2024","unstructured":"Gupta, M., Wadhvani, R., & Rasool, A. (2024). Comprehensive analysis of change-point dynamics detection in time series data: A review. Expert Systems with Applications, 248, Article 123342.","journal-title":"Expert Systems with Applications"},{"key":"7000_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110158","volume":"260","author":"S Hao","year":"2023","unstructured":"Hao, S., Wang, Z., Alexander, A. D., Yuan, J., & Zhang, W. (2023). Micos: Mixed supervised contrastive learning for multivariate time series classification. Knowledge-Based Systems, 260, Article 110158.","journal-title":"Knowledge-Based Systems"},{"key":"7000_CR24","unstructured":"Henaff, O. (2020). Data-efficient image recognition with contrastive predictive coding. In: International conference on machine learning, pp. 4182\u20134192. PMLR"},{"key":"7000_CR25","doi-asserted-by":"crossref","unstructured":"Hido, S., Id\u00e9, T., Kashima, H., Kubo, H., & Matsuzawa, H. (2008). Unsupervised change analysis using supervised learning. In: Proceedings of the 12th Pacific-Asia conference on advances in knowledge discovery and data mining. PAKDD\u201908, pp. 148\u2013159. Springer, Berlin, Heidelberg. http:\/\/dl.acm.org\/citation.cfm?id=1786574.1786592","DOI":"10.1007\/978-3-540-68125-0_15"},{"key":"7000_CR26","doi-asserted-by":"crossref","unstructured":"Hushchyn, M., & Ustyuzhanin, A. (2020). Generalization of change-point detection in time series data based on direct density ratio estimation","DOI":"10.1016\/j.jocs.2021.101385"},{"issue":"3","key":"7000_CR27","doi-asserted-by":"publisher","first-page":"1604","DOI":"10.1109\/TNSE.2022.3148276","volume":"9","author":"Y Jiao","year":"2022","unstructured":"Jiao, Y., Yang, K., Song, D., & Tao, D. (2022). Timeautoad: Autonomous anomaly detection with self-supervised contrastive loss for multivariate time series. IEEE Transactions on Network Science and Engineering, 9(3), 1604\u20131619.","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"7000_CR28","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1145\/1577069.1755831","volume":"10","author":"T Kanamori","year":"2009","unstructured":"Kanamori, T., Hido, S., & Sugiyama, M. (2009). A least-squares approach to direct importance estimation. Journal of Machine Learning Research, 10, 1391\u20131445. https:\/\/doi.org\/10.1145\/1577069.1755831","journal-title":"Journal of Machine Learning Research"},{"key":"7000_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-012-5323-6","author":"T Kanamori","year":"2013","unstructured":"Kanamori, T., Suzuki, T., & Sugiyama, M. (2013). Computational complexity of kernel-based density-ratio estimation: A condition number analysis. Machine Learning. https:\/\/doi.org\/10.1007\/s10994-012-5323-6","journal-title":"Machine Learning"},{"issue":"2","key":"7000_CR30","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1111\/j.1365-2966.2010.17325.x","volume":"409","author":"MJ Keith","year":"2010","unstructured":"Keith, M. J., Jameson, A., Straten, W., Bailes, M., Johnston, S., Kramer, M., Possenti, A., Bates, S. D., Bhat, N. D. R., Burgay, M., Burke-Spolaor, S., D\u2019Amico, N., Levin, L., McMahon, P. L., Milia, S., & Stappers, B. W. (2010). The high time resolution universe pulsar survey - i. System configuration and initial discoveries. Monthly Notices of the Royal Astronomical Society, 409(2), 619\u2013627. https:\/\/doi.org\/10.1111\/j.1365-2966.2010.17325.x","journal-title":"Monthly Notices of the Royal Astronomical Society"},{"key":"7000_CR31","unstructured":"Kepler and K2 science center: Kepler and K2 data products. https:\/\/keplerscience.arc.nasa.gov\/data-products.html (2019)"},{"key":"7000_CR32","unstructured":"Khan, H., Marcuse, L. & Yener, B. (2019). Deep density ratio estimation for change point detection"},{"issue":"500","key":"7000_CR33","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1080\/01621459.2012.737745","volume":"107","author":"R Killick","year":"2012","unstructured":"Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590\u20131598. https:\/\/doi.org\/10.1080\/01621459.2012.737745","journal-title":"Journal of the American Statistical Association"},{"key":"7000_CR34","unstructured":"Kingma, D.P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"issue":"1","key":"7000_CR35","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1740\/1\/012050","volume":"1740","author":"PS Kostenetskiy","year":"2021","unstructured":"Kostenetskiy, P. S., Chulkevich, R. A., & Kozyrev, V. I. (2021). Hpc resources of the higher school of economics. Journal of Physics: Conference Series, 1740(1), Article 012050. https:\/\/doi.org\/10.1088\/1742-6596\/1740\/1\/012050","journal-title":"Journal of Physics: Conference Series"},{"key":"7000_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3155969","volume":"60","author":"F Lattari","year":"2022","unstructured":"Lattari, F., Rucci, A., & Matteucci, M. (2022). A deep learning approach for change points detection in insar time series. IEEE Transactions on Geoscience and Remote Sensing, 60, 1\u201316.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"7000_CR37","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.neunet.2013.01.012","volume":"43","author":"S Liu","year":"2013","unstructured":"Liu, S., Yamada, M., Collier, N., & Sugiyama, M. (2013). Change-point detection in time-series data by relative density-ratio estimation. Neural Networks, 43, 72\u201383. https:\/\/doi.org\/10.1016\/j.neunet.2013.01.012","journal-title":"Neural Networks"},{"key":"7000_CR38","doi-asserted-by":"publisher","first-page":"112092","DOI":"10.1109\/ACCESS.2020.3002257","volume":"8","author":"S Lu","year":"2020","unstructured":"Lu, S., & Huang, S. (2020). Segmentation of multivariate industrial time series data based on dynamic latent variable predictability. IEEE Access, 8, 112092\u2013112103.","journal-title":"IEEE Access"},{"issue":"1","key":"7000_CR39","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1093\/mnras\/stw656","volume":"459","author":"RJ Lyon","year":"2016","unstructured":"Lyon, R. J., Stappers, B. W., Cooper, S., Brooke, J. M., & Knowles, J. D. (2016). Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach. Monthly Notices of the Royal Astronomical Society, 459(1), 1104\u20131123. https:\/\/doi.org\/10.1093\/mnras\/stw656","journal-title":"Monthly Notices of the Royal Astronomical Society"},{"key":"7000_CR40","doi-asserted-by":"crossref","unstructured":"Ma, X., Lai, L., & Cui, S. (2021). A deep q-network based approach for online Bayesian change point detection. In: 2021 IEEE 31st international workshop on machine learning for signal processing (MLSP), pp. 1\u20136. IEEE","DOI":"10.1109\/MLSP52302.2021.9596490"},{"key":"7000_CR41","doi-asserted-by":"publisher","unstructured":"Nam, H. & Sugiyama, M. (2015). Direct density ratio estimation with convolutional neural networks with application in outlier detection. IEICE Transactions on Information and Systems E98.D, 1073\u20131079 https:\/\/doi.org\/10.1587\/transinf.2014EDP7335","DOI":"10.1587\/transinf.2014EDP7335"},{"issue":"1\/2","key":"7000_CR42","doi-asserted-by":"publisher","first-page":"100","DOI":"10.2307\/2333009","volume":"41","author":"ES Page","year":"1954","unstructured":"Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41(1\/2), 100\u2013115.","journal-title":"Biometrika"},{"issue":"3\u20134","key":"7000_CR43","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1093\/biomet\/42.3-4.523","volume":"42","author":"ES Page","year":"1955","unstructured":"Page, E. S. (1955). A test for a change in a parameter occurring at an unknown point. Biometrika, 42(3\u20134), 523\u2013527. https:\/\/doi.org\/10.1093\/biomet\/42.3-4.523","journal-title":"Biometrika"},{"key":"7000_CR44","unstructured":"Puchkin, N., & Shcherbakova, V. (2023). A contrastive approach to online change point detection. In: International conference on artificial intelligence and statistics, pp. 5686\u20135713. PMLR"},{"issue":"336","key":"7000_CR45","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","volume":"66","author":"WM Rand","year":"1971","unstructured":"Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336), 846\u2013850. https:\/\/doi.org\/10.1080\/01621459.1971.10482356","journal-title":"Journal of the American Statistical Association"},{"key":"7000_CR46","doi-asserted-by":"crossref","unstructured":"Romanenkova, E., Stepikin, A., Morozov, M., & Zaytsev, A. (2022). Indid: Instant disorder detection via a principled neural network. In: Proceedings of the 30th ACM international conference on multimedia, pp. 3152\u20133162","DOI":"10.1145\/3503161.3548182"},{"key":"7000_CR47","doi-asserted-by":"publisher","first-page":"104700","DOI":"10.1109\/ACCESS.2023.3318318","volume":"11","author":"A Ryzhikov","year":"2023","unstructured":"Ryzhikov, A., Hushchyn, M., & Derkach, D. (2023). Latent stochastic differential equations for change point detection. IEEE Access, 11, 104700\u2013104711.","journal-title":"IEEE Access"},{"key":"7000_CR48","unstructured":"Sugiyama, M., Nakajima, S., Kashima, H., B\u00fcnau, P., & Kawanabe, M. (2007). Direct importance estimation with model selection and its application to covariate shift adaptation., vol. 20"},{"key":"7000_CR49","doi-asserted-by":"publisher","DOI":"10.1007\/s10463-011-0343-8","author":"M Sugiyama","year":"2011","unstructured":"Sugiyama, M., Suzuki, T., & Kanamori, T. (2011). Density ratio matching under the Bregman divergence: A unified framework of density ratio estimation. Annals of the Institute of Statistical Mathematics. https:\/\/doi.org\/10.1007\/s10463-011-0343-8","journal-title":"Annals of the Institute of Statistical Mathematics"},{"key":"7000_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103094","volume":"127","author":"C Tang","year":"2023","unstructured":"Tang, C., Xu, L., Yang, B., Tang, Y., & Zhao, D. (2023). Gru-based interpretable multivariate time series anomaly detection in industrial control system. Computers & Security, 127, Article 103094.","journal-title":"Computers & Security"},{"key":"7000_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.107299","volume":"167","author":"C Truong","year":"2020","unstructured":"Truong, C., Oudre, L., & Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167, Article 107299. https:\/\/doi.org\/10.1016\/j.sigpro.2019.107299","journal-title":"Signal Processing"},{"key":"7000_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106196","volume":"173","author":"J Wan","year":"2024","unstructured":"Wan, J., Xia, N., Yin, Y., Pan, X., Hu, J., & Yi, J. (2024). Tcdformer: A transformer framework for non-stationary time series forecasting based on trend and change-point detection. Neural Networks, 173, Article 106196.","journal-title":"Neural Networks"},{"key":"7000_CR53","unstructured":"Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research 10(2)"},{"key":"7000_CR54","doi-asserted-by":"publisher","first-page":"133190","DOI":"10.1109\/ACCESS.2019.2940729","volume":"7","author":"GM Weiss","year":"2019","unstructured":"Weiss, G. M., Yoneda, K., & Hayajneh, T. (2019). Smartphone and smartwatch-based biometrics using activities of daily living. IEEE Access, 7, 133190\u2013133202.","journal-title":"IEEE Access"},{"issue":"23","key":"7000_CR55","doi-asserted-by":"publisher","first-page":"28655","DOI":"10.1007\/s10489-023-04985-8","volume":"53","author":"B Xue","year":"2023","unstructured":"Xue, B., Gao, X., Zhai, F., Li, B., Yu, J., Fu, S., Chen, L., & Meng, Z. (2023). A contrastive autoencoder with multi-resolution segment-consistency discrimination for multivariate time series anomaly detection. Applied Intelligence, 53(23), 28655\u201328674.","journal-title":"Applied Intelligence"},{"issue":"1","key":"7000_CR56","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s42524-025-4109-z","volume":"12","author":"R Xu","year":"2025","unstructured":"Xu, R., Song, Z., Wu, J., Wang, C., & Zhou, S. (2025). Change-point detection with deep learning: A review. Frontiers of Engineering Management, 12(1), 154\u2013176.","journal-title":"Frontiers of Engineering Management"},{"issue":"5","key":"7000_CR57","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.1162\/NECO_a_00442","volume":"25","author":"M Yamada","year":"2013","unstructured":"Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., & Sugiyama, M. (2013). Relative density-ratio estimation for robust distribution comparison. Neural Computation, 25(5), 1324\u20131370. https:\/\/doi.org\/10.1162\/NECO_a_00442","journal-title":"Neural Computation"},{"key":"7000_CR58","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, C., Zhou, T., Wen, Q., & Sun, L. (2023). Dcdetector: Dual attention contrastive representation learning for time series anomaly detection. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, pp. 3033\u20133045","DOI":"10.1145\/3580305.3599295"},{"key":"7000_CR59","unstructured":"Zinkevich, M. (2003). Online convex programming and generalized infinitesimal gradient ascent. In: Proceedings of the twentieth international conference on international conference on machine learning. ICML\u201903, pp. 928\u2013935. AAAI Press"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-026-07000-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-026-07000-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-026-07000-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:30:38Z","timestamp":1778081438000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-026-07000-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":59,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["7000"],"URL":"https:\/\/doi.org\/10.1007\/s10994-026-07000-6","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"24 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"56"}}