{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:28:07Z","timestamp":1775838487305,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["400845550"],"award-info":[{"award-number":["400845550"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Johannes Kepler University Linz"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While in theory, any order is sufficient, in practice, this order has a substantial impact on the quality of the final prediction. Dynamic classifier chains denote the idea that for each instance to classify, the order in which the labels are predicted is dynamically chosen. The complexity of a na\u00efve implementation of such an approach is prohibitive, because it would require to train a sequence of classifiers for every possible permutation of the labels. To tackle this problem efficiently, we propose a new approach based on random decision trees which can dynamically select the label ordering for each prediction. We show empirically that a dynamic selection of the next label improves over the use of a static ordering under an otherwise unchanged random decision tree model. In addition, we also demonstrate an alternative approach based on extreme gradient boosted trees, which allows for a more target-oriented training of dynamic classifier chains. Our results show that this variant outperforms random decision trees and other tree-based multi-label classification methods. More importantly, the dynamic selection strategy allows to considerably speed up training and prediction.<\/jats:p>","DOI":"10.1007\/s10994-022-06162-3","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T21:29:53Z","timestamp":1648243793000},"page":"4129-4165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Tree-based dynamic classifier chains"],"prefix":"10.1007","volume":"112","author":[{"given":"Eneldo","family":"Loza Menc\u00eda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moritz","family":"Kulessa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Bohlender","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1207-0159","authenticated-orcid":false,"given":"Johannes","family":"F\u00fcrnkranz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"6162_CR1","doi-asserted-by":"crossref","unstructured":"Bogatinovski, J., Todorovski, L., Dzeroski, S., Kocev, D. (2021). Comprehensive comparative study of multi-label classification methods. CoRR https:\/\/arxiv.org\/abs\/2102.07113","DOI":"10.1016\/j.eswa.2022.117215"},{"key":"6162_CR2","doi-asserted-by":"crossref","unstructured":"Bohlender, S., Loza\u00a0Menc\u00eda, E., Kulessa, M.(2020). Extreme gradient boosted multi-label trees for dynamic classifier chains. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds.) Proceedings of the 23rd International Conference of Discovery Science (DS-20). pp. 471\u2013485. Springer, Thessaloniki, Greece , https:\/\/doi.org\/10.1007\/978-3-030-61527-7_31","DOI":"10.1007\/978-3-030-61527-7_31"},{"key":"6162_CR3","doi-asserted-by":"crossref","unstructured":"Boutell, M.R., Luo, J., Shen, X., Brown, C.M.C.M. (2004). Learning multi-label scene classification. Pattern Recognition 37(9), 1757\u20131771 , http:\/\/www.rose-hulman.edu\/~boutell\/publications\/boutell04PRmultilabel.pdf","DOI":"10.1016\/j.patcog.2004.03.009"},{"issue":"1","key":"6162_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332.","journal-title":"Machine Learning"},{"key":"6162_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C .(2016). XGBoost: A scalable tree boosting system. In: Proc. of the 22nd SIGKDD Int. Conf. on Knowledge Discovery and Data Mining. pp. 785\u2013794. ACM","DOI":"10.1145\/2939672.2939785"},{"key":"6162_CR6","doi-asserted-by":"crossref","unstructured":"da Silva, P.N., Gon\u00e7alves, E.C., Plastino, A., Freitas, A.A.(2014). Distinct chains for different instances: An effective strategy for multi-label classifier chains. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML\/PKDD). pp. 453\u2013468. Springer","DOI":"10.1007\/978-3-662-44851-9_29"},{"key":"6162_CR7","unstructured":"Dembczy\u0144ski, K., Cheng, W., H\u00fcllermeier, E.(2010). Bayes optimal multilabel classification via probabilistic classifier chains. In: Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML). pp. 279\u2013286"},{"issue":"1\u20132","key":"6162_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10994-012-5285-8","volume":"88","author":"K Dembczy\u0144ski","year":"2012","unstructured":"Dembczy\u0144ski, K., Waegeman, W., Cheng, W., & H\u00fcllermeier, E. (2012). On label dependence and loss minimization in multi-label classification. Machine Learning, 88(1\u20132), 5\u201345.","journal-title":"Machine Learning"},{"key":"6162_CR9","unstructured":"Fan, W., Greengrass, E., McCloskey, J., Yu, P.S., Drammey, K.(2005). Effective estimation of posterior probabilities: Explaining the accuracy of randomized decision tree approaches. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM). pp. 154\u2013161"},{"key":"6162_CR10","doi-asserted-by":"crossref","unstructured":"Fan, W., Wang, H., Yu, P.S., Ma, S.(2003). Is random model better? On its accuracy and efficiency. In: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM). pp. 51\u201358","DOI":"10.1109\/ICDM.2003.1250902"},{"issue":"4","key":"6162_CR11","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367\u2013378.","journal-title":"Computational Statistics & Data Analysis"},{"issue":"1","key":"6162_CR12","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1023\/A:1006524209794","volume":"13","author":"J F\u00fcrnkranz","year":"1999","unstructured":"F\u00fcrnkranz, J. (1999). Separate-and-conquer rule learning. Artificial Intelligence Review, 13(1), 3\u201354.","journal-title":"Artificial Intelligence Review"},{"key":"6162_CR13","doi-asserted-by":"crossref","unstructured":"Godbole, S., Sarawagi, S.(2004). Discriminative methods for multi-labeled classification. In: Advances in Knowledge Discovery and Data Mining, 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings. pp. 22\u201330","DOI":"10.1007\/978-3-540-24775-3_5"},{"key":"6162_CR14","doi-asserted-by":"crossref","unstructured":"Goncalves, E.C., Plastino, A., Freitas, A.A.(2013). A Genetic Algorithm for Optimizing the Label Ordering in Multi-label Classifier Chains. In: Proceedings of the IEEE 25th International Conference on Tools with Artificial Intelligence. pp. 469\u2013476","DOI":"10.1109\/ICTAI.2013.76"},{"key":"6162_CR15","doi-asserted-by":"crossref","unstructured":"Joachims, T.(1998). Text categorization with suport vector machines: Learning with many relevant features. In: Machine Learning: ECML-98, 10th European Conference on Machine Learning (LNCS 1398). pp. 137\u2013142. Springer , hdl.handle.net\/2003\/2595","DOI":"10.1007\/BFb0026683"},{"key":"6162_CR16","doi-asserted-by":"crossref","unstructured":"Kong, X., Yu, P.S. (2011). An Ensemble-based Approach to Fast Classification of Multi-label Data Streams. In: Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing. pp. 95\u2013104 (October)","DOI":"10.4108\/icst.collaboratecom.2011.247086"},{"key":"6162_CR17","doi-asserted-by":"crossref","unstructured":"Kulessa, M., Loza\u00a0Menc\u00eda, E.(2018). Dynamic classifier chain with random decision trees. In: Proceedings of the 21st International Conference of Discovery Science (DS-18)","DOI":"10.1007\/978-3-030-01771-2_3"},{"issue":"1","key":"6162_CR18","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s10994-013-5371-6","volume":"92","author":"A Kumar","year":"2013","unstructured":"Kumar, A., Vembu, S., Menon, A. K., & Elkan, C. (2013). Beam search algorithms for multilabel learning. Machine Learning, 92(1), 65\u201389.","journal-title":"Machine Learning"},{"key":"6162_CR19","doi-asserted-by":"crossref","unstructured":"Li, N., Zhou, Z. (2013). Selective Ensemble of Classifier Chains. In: Multiple Classifier Systems: 11th International Workshop on Multiple Classifier Systems, pp. 146\u2013156","DOI":"10.1007\/978-3-642-38067-9_13"},{"key":"6162_CR20","first-page":"712","volume":"28","author":"W Liu","year":"2015","unstructured":"Liu, W., & Tsang, I. (2015). On the optimality of classifier chain for multi-label classification. Advances in Neural Information Processing Systems, 28, 712\u2013720.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"6162_CR21","doi-asserted-by":"crossref","unstructured":"Llerena, J.V., Deratani Mau\u00e1, D.(2017). On using sum-product networks for multi-label classification. In: Proc. of the Brazilian Conference on Intelligent Systems (BRACIS). pp. 25\u201330","DOI":"10.1109\/BRACIS.2017.34"},{"issue":"1","key":"6162_CR22","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10994-016-5552-1","volume":"105","author":"E Loza Menc\u00eda","year":"2016","unstructured":"Loza Menc\u00eda, E., & Janssen, F. (2016). Learning rules for multi-label classification: a stacking and a separate-and-conquer approach. Machine Learning, 105(1), 77\u2013126.","journal-title":"Machine Learning"},{"issue":"7\u20139","key":"6162_CR23","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1016\/j.neucom.2009.11.024","volume":"73","author":"E Loza Menc\u00eda","year":"2010","unstructured":"Loza Menc\u00eda, E., Park, S. H., & F\u00fcrnkranz, J. (2010). Efficient voting prediction for pairwise multilabel classification. Neurocomputing, 73(7\u20139), 1164\u20131176.","journal-title":"Neurocomputing"},{"issue":"9","key":"6162_CR24","doi-asserted-by":"publisher","first-page":"3084","DOI":"10.1016\/j.patcog.2012.03.004","volume":"45","author":"G Madjarov","year":"2012","unstructured":"Madjarov, G., Kocev, D., Gjorgjevikj, D., & D\u017eeroski, S. (2012). An extensive experimental comparison of methods for multi-label learning. Pattern Recognition, 45(9), 3084\u20133104.","journal-title":"Pattern Recognition"},{"key":"6162_CR25","unstructured":"Malerba, D., Semeraro, G., Esposito, F.(1997). A multistrategy approach to learning multiple dependent concepts. In: Machine Learning and Statistics: The Interface, chap.\u00a04, pp. 87\u2013106"},{"key":"6162_CR26","unstructured":"Mena, D., Monta\u00f1\u00e9s, E., Quevedo, J.R., Coz, J.J.d.(2015). Using A* for inference in probabilistic classifier chains. In: Proceedings of the 24th International Conference on Artificial Intelligence. pp. 3707\u20133713"},{"key":"6162_CR27","doi-asserted-by":"crossref","unstructured":"Mena, D., Monta\u00f1\u00e9s, E., Quevedo, J.R., Coz, J.J.d.(2016). An overview of inference methods in probabilistic classifier chains for multilabel classification. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6(6), 215\u2013230","DOI":"10.1002\/widm.1185"},{"key":"6162_CR28","doi-asserted-by":"crossref","unstructured":"Moyano, J.M., Gibaja, E.L., Ventura, S.(2017). MLDA: A tool for analyzing multi-label datasets. Knowledge-Based Systems 121, 1\u20133 , https:\/\/github.com\/i02momuj\/MLDA","DOI":"10.1016\/j.knosys.2017.01.018"},{"key":"6162_CR29","doi-asserted-by":"crossref","unstructured":"Nam, J., Kim, J., Loza\u00a0Menc\u00eda, E., Gurevych, I., F\u00fcrnkranz, J.(2014). Large-scale multi-label text classification - revisiting neural networks. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML\/PKDD). pp. 437\u2013452","DOI":"10.1007\/978-3-662-44851-9_28"},{"key":"6162_CR30","unstructured":"Nam, J., Kim, Y., Loza\u00a0Menc\u00eda, E., Park, S., Sarikaya, R., F\u00fcrnkranz, J.(2019). Learning context-dependent label permutations for multi-label classification. In: Proceedings of the 36th International Conference on Machine Learning (ICML-19). pp. 4733\u20134742"},{"key":"6162_CR31","unstructured":"Nam, J., Loza\u00a0Menc\u00eda, E., Kim, H.J., F\u00fcrnkranz, J.(2017). Maximizing subset accuracy with recurrent neural networks in multi-label classification. In: Advances in Neural Information Processing Systems 30 (NIPS-17). pp. 5419\u20135429"},{"key":"6162_CR32","doi-asserted-by":"crossref","unstructured":"Nguyen, V.L., H\u00fcllermeier, E., Rapp, M., Loza\u00a0Menc\u00eda, E., F\u00fcrnkranz, J.(2020). On aggregation in ensembles of multilabel classifiers. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds.) Proceedings of the 23rd International Conference on Discovery Science. pp. 533\u2013547. Springer, Cham (Oct)","DOI":"10.1007\/978-3-030-61527-7_35"},{"key":"6162_CR33","doi-asserted-by":"crossref","unstructured":"Papagiannopoulou, C., Tsoumakas, G., Tsamardinos, I.(2015). Discovering and exploiting deterministic label relationships in multi-label learning. In: Proc. of the 21th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining. pp. 915\u2013924","DOI":"10.1145\/2783258.2783302"},{"issue":"2","key":"6162_CR34","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1016\/j.patcog.2011.08.007","volume":"45","author":"JR Quevedo","year":"2012","unstructured":"Quevedo, J. R., Luaces, O., & Bahamonde, A. (2012). Multilabel classifiers with a probabilistic thresholding strategy. Pattern Recognition, 45(2), 876\u2013883.","journal-title":"Pattern Recognition"},{"key":"6162_CR35","doi-asserted-by":"crossref","unstructured":"Rapp, M., Loza\u00a0Menc\u00eda, E., F\u00fcrnkranz, J., Nguyen, V.L., H\u00fcllermeier, E.(2020). Learning gradient boosted multi-label classification rules. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML\/PKDD)","DOI":"10.1007\/978-3-030-67664-3_8"},{"issue":"3","key":"6162_CR36","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1016\/j.patcog.2013.10.006","volume":"47","author":"J Read","year":"2014","unstructured":"Read, J., Martino, L., & Luengo, D. (2014). Efficient Monte Carlo methods for multi-dimensional learning with classifier chains. Pattern Recognition, 47(3), 1535\u20131546.","journal-title":"Pattern Recognition"},{"issue":"3","key":"6162_CR37","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","volume":"85","author":"J Read","year":"2011","unstructured":"Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier chains for multi-label classification. Machine Learning, 85(3), 333\u2013359.","journal-title":"Machine Learning"},{"key":"6162_CR38","doi-asserted-by":"crossref","unstructured":"Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2021). Classifier chains: A review and perspectives. Journal of Artificial Intelligence Research, 70, 683\u2013718.","DOI":"10.1613\/jair.1.12376"},{"issue":"2\/3","key":"6162_CR39","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1023\/A:1007649029923","volume":"39","author":"RE Schapire","year":"2000","unstructured":"Schapire, R. E., & Singer, Y. (2000). Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2\/3), 135\u2013168.","journal-title":"Machine Learning"},{"key":"6162_CR40","doi-asserted-by":"crossref","unstructured":"Senge, R., Del\u00a0Coz, J.J., H\u00fcllermeier, E.(2014). On the problem of error propagation in classifier chains for multi-label classification. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds.) Data Analysis, Machine Learning and Knowledge Discovery, pp. 163\u2013170","DOI":"10.1007\/978-3-319-01595-8_18"},{"key":"6162_CR41","unstructured":"Si, S., Zhang, H., Keerthi, S.S., Mahajan, D., Dhillon, I.S., Hsieh, C.J. (2017). Gradient boosted decision trees for high dimensional sparse output. In: Proceedings of the 34th International Conference on Machine Learning (ICML). pp. 3182\u20133190. PMLR"},{"key":"6162_CR42","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.patrec.2013.11.007","volume":"41","author":"LE Sucar","year":"2014","unstructured":"Sucar, L. E., Bielza, C., Morales, E. F., Hernandez-Leal, P., Zaragoza, J. H., & Larra\u00f1aga, P. (2014). Multi-label classification with Bayesian network-based chain classifiers. Pattern Recognition Letters, 41, 14\u201322.","journal-title":"Pattern Recognition Letters"},{"key":"6162_CR43","doi-asserted-by":"crossref","unstructured":"Trajdos, P., Kurzynski, M.(2019). Dynamic classifier chains for multi-label learning. In: Fink, G.A., Frintrop, S., Jiang, X. (eds.) Proceedings of the 41st DAGM German Conference on Pattern Recognition (GCPR). pp. 567\u2013580. Springer","DOI":"10.1007\/978-3-030-33676-9_40"},{"key":"6162_CR44","doi-asserted-by":"crossref","unstructured":"Tsoumakas, G., Katakis, I., Vlahavas, I.(2010). Mining Multi-label Data. In: Data Mining and Knowledge Discovery Handbook, pp. 667\u2013685","DOI":"10.1007\/978-0-387-09823-4_34"},{"issue":"3","key":"6162_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jdwm.2007070101","volume":"3","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas, G., & Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1\u201317.","journal-title":"International Journal of Data Warehousing and Mining"},{"issue":"2","key":"6162_CR46","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s10994-008-5077-3","volume":"73","author":"C Vens","year":"2008","unstructured":"Vens, C., Struyf, J., Schietgat, L., D\u017eeroski, S., & Blockeel, H. (2008). Decision trees for hierarchical multi-label classification. Machine Learning, 73(2), 185.","journal-title":"Machine Learning"},{"issue":"2","key":"6162_CR47","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s10618-018-0595-5","volume":"33","author":"W Waegeman","year":"2019","unstructured":"Waegeman, W., Dembczy\u0144ski, K., & H\u00fcllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery, 33(2), 293\u2013324.","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"8","key":"6162_CR48","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M Zhang","year":"2014","unstructured":"Zhang, M., & Zhou, Z. (2014). A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 26(8), 1819\u20131837.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"6162_CR49","unstructured":"Zhang, X., Fan, W., Du, N.(2015). Random decision hashing for massive data learning. In: Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications. pp. 65\u201380"},{"key":"6162_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yuan, Q., Zhao, S., Fan, W., Zheng, W., Wang, Z.(2010). Multi-label classification without the multi-label cost. In: Proceedings of the SIAM International Conference on Data Mining (SDM). pp. 778\u2013789","DOI":"10.1137\/1.9781611972801.68"},{"key":"6162_CR51","unstructured":"Zhang, Z., Jung, C.(2019). GBDT-MO: Gradient Boosted Decision Trees for Multiple Outputs. http:\/\/arxiv.org\/abs\/1909.04373"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06162-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06162-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06162-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T17:09:11Z","timestamp":1698253751000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06162-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,25]]},"references-count":51,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["6162"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06162-3","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,25]]},"assertion":[{"value":"9 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2022","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}