{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:37:32Z","timestamp":1791236252267,"version":"4.1.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Convolutional neural networks (CNNs) have achieved impressive results on imbalanced image data, but they still have difficulty generalizing to minority classes and their decisions are difficult to interpret. These problems are related because the method by which CNNs generalize to minority classes, which requires improvement, is wrapped in a black-box. To demystify CNN decisions on imbalanced data, we focus on their latent features. Although CNNs embed the pattern knowledge learned from a training set in model parameters, the\n                    <jats:italic>effect<\/jats:italic>\n                    of this knowledge is contained in\n                    <jats:italic>feature and classification embeddings<\/jats:italic>\n                    (\n                    <jats:italic>FE<\/jats:italic>\n                    and\n                    <jats:italic>CE<\/jats:italic>\n                    ). These embeddings can be extracted from a trained model and their global, class properties (e.g., frequency, magnitude and identity) can be analyzed. We find that important information regarding the ability of a neural network to generalize to minority classes resides in the\n                    <jats:italic>class top-K CE and FE<\/jats:italic>\n                    . We show that a CNN learns a limited number of\n                    <jats:italic>class top-K CE<\/jats:italic>\n                    per category, and that their magnitudes vary based on whether the same class is balanced or imbalanced. We hypothesize that latent class\n                    <jats:italic>diversity<\/jats:italic>\n                    is as important as the number of class examples, which has important implications for re-sampling and cost-sensitive methods. These methods generally focus on rebalancing model weights, class numbers and margins; instead of diversifying class latent features. We also demonstrate that a CNN has difficulty generalizing to test data if the magnitude of its top-K latent features do not match the training set. We use three popular image datasets and two cost-sensitive algorithms commonly employed in imbalanced learning for our experiments.\n                  <\/jats:p>","DOI":"10.1007\/s10994-023-06326-9","type":"journal-article","created":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T11:03:46Z","timestamp":1681211026000},"page":"4785-4810","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Understanding CNN fragility when learning with imbalanced data"],"prefix":"10.1007","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0878-8560","authenticated-orcid":false,"given":"Damien","family":"Dablain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristen N.","family":"Jacobson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin","family":"Bellinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Roberts","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nitesh V.","family":"Chawla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,11]]},"reference":[{"key":"6326_CR1","doi-asserted-by":"crossref","unstructured":"Achtibat, R., Dreyer, M., Eisenbraun, I., Bosse, S., Wiegand, T., Samek, W., Lapuschkin, S. (2022). From \u201cwhere\u201d to \u201cwhat\u201d: Towards human-understandable explanations through concept relevance propagation. arXiv preprint arXiv:2206.03208","DOI":"10.1038\/s42256-023-00711-8"},{"key":"6326_CR2","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138\u201352160.","journal-title":"IEEE Access"},{"issue":"6","key":"6326_CR3","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1109\/72.286891","volume":"4","author":"R Anand","year":"1993","unstructured":"Anand, R., Mehrotra, K. G., Mohan, C. K., & Ranka, S. (1993). An improved algorithm for neural network classification of imbalanced training sets. IEEE Transactions on Neural Networks, 4(6), 962\u2013969.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"6326_CR4","unstructured":"Badola, A., Roy, C., Padmanabhan, V., & Lal, R. (2021). Identifying class specific filters with L1 norm frequency histograms in deep CNNs. arXiv preprint arXiv:2112.07719"},{"key":"6326_CR5","doi-asserted-by":"crossref","unstructured":"Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A. (2017). Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6541\u20136549).","DOI":"10.1109\/CVPR.2017.354"},{"key":"6326_CR6","doi-asserted-by":"crossref","unstructured":"Bauder, R. A., Khoshgoftaar, T. M., Hasanin, T. (2018). An empirical study on class rarity in big data. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 785\u2013790). IEEE","DOI":"10.1109\/ICMLA.2018.00125"},{"key":"6326_CR7","unstructured":"Bellinger, C., Corizzo, R., & Japkowicz, N. (2020). Remix: Calibrated resampling for class imbalance in deep learning. arXiv preprint arXiv:2012.02312"},{"issue":"3","key":"6326_CR8","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/s10994-017-5670-4","volume":"107","author":"C Bellinger","year":"2018","unstructured":"Bellinger, C., Drummond, C., & Japkowicz, N. (2018). Manifold-based synthetic oversampling with manifold conformance estimation. Machine Learning, 107(3), 605\u2013637.","journal-title":"Machine Learning"},{"issue":"10","key":"6326_CR9","doi-asserted-by":"publisher","first-page":"1997","DOI":"10.1109\/TNNLS.2015.2496947","volume":"27","author":"PP Brahma","year":"2015","unstructured":"Brahma, P. P., Wu, D., & She, Y. (2015). Why deep learning works: A manifold disentanglement perspective. IEEE Transactions on Neural Networks and Learning Systems, 27(10), 1997\u20132008.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"6326_CR10","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249\u2013259.","journal-title":"Neural Networks"},{"key":"6326_CR11","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T. (2019). Learning imbalanced datasets with label-distribution-aware margin loss. In Advances in neural information processing systems (Vol. 32)."},{"key":"6326_CR12","unstructured":"Cayton, L. (2005). Algorithms for manifold learning. Univ. of California at San Diego, Tech. Rep 12(1-17), 1"},{"key":"6326_CR13","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321\u2013357.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"6326_CR14","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3389\/fams.2018.00012","volume":"4","author":"CK Chui","year":"2018","unstructured":"Chui, C. K., & Mhaskar, H. N. (2018). Deep nets for local manifold learning. Frontiers in Applied Mathematics and Statistics, 4, 12.","journal-title":"Frontiers in Applied Mathematics and Statistics"},{"key":"6326_CR15","doi-asserted-by":"publisher","unstructured":"Dablain, D., Bellinger, C., Krawczyk, B., & Chawla, N. (2022). Efficient augmentation for imbalanced deep learning. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2207.06080","DOI":"10.48550\/ARXIV.2207.06080"},{"issue":"3","key":"6326_CR16","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.neuron.2012.01.010","volume":"73","author":"JJ DiCarlo","year":"2012","unstructured":"DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012). How does the brain solve visual object recognition? Neuron, 73(3), 415\u2013434.","journal-title":"Neuron"},{"issue":"1","key":"6326_CR17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/j.0824-7935.2004.t01-1-00228.x","volume":"20","author":"A Estabrooks","year":"2004","unstructured":"Estabrooks, A., Jo, T., & Japkowicz, N. (2004). A multiple resampling method for learning from imbalanced data sets. Computational Intelligence, 20(1), 18\u201336.","journal-title":"Computational Intelligence"},{"key":"6326_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-98074-4","volume-title":"Learning from imbalanced data sets","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez, A., Garc\u00eda, S., Galar, M., Prati, R. C., Krawczyk, B., & Herrera, F. (2018). Learning from imbalanced data sets (Vol. 10). Springer."},{"issue":"11","key":"6326_CR19","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","volume":"2","author":"R Geirhos","year":"2020","unstructured":"Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., & Wichmann, F. A. (2020). Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11), 665\u2013673.","journal-title":"Nature Machine Intelligence"},{"issue":"2","key":"6326_CR20","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1609\/aimag.v40i2.2850","volume":"40","author":"D Gunning","year":"2019","unstructured":"Gunning, D., & Aha, D. (2019). DARPA\u2019s explainable artificial intelligence (XAI) program. AI Magazine, 40(2), 44\u201358.","journal-title":"AI Magazine"},{"key":"6326_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"issue":"9","key":"6326_CR22","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263\u20131284.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"11","key":"6326_CR23","doi-asserted-by":"publisher","first-page":"2781","DOI":"10.1109\/TPAMI.2019.2914680","volume":"42","author":"C Huang","year":"2019","unstructured":"Huang, C., Li, Y., Loy, C. C., & Tang, X. (2019). Deep imbalanced learning for face recognition and attribute prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(11), 2781\u20132794.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"9","key":"6326_CR24","doi-asserted-by":"publisher","first-page":"1967","DOI":"10.1167\/jov.21.9.1967","volume":"21","author":"LS Huber","year":"2021","unstructured":"Huber, L. S., Geirhos, R., & Wichmann, F. A. (2021). The developmental trajectory of object recognition robustness: Comparing children, adults, and CNNs. Journal of Vision, 21(9), 1967\u20131967.","journal-title":"Journal of Vision"},{"key":"6326_CR25","unstructured":"Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., & Madry, A. (2019). Adversarial examples are not bugs, they are features. In Advances in neural information processing systems (Vol. 32)."},{"issue":"1","key":"6326_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6(1), 1\u201354.","journal-title":"Journal of Big Data"},{"key":"6326_CR27","unstructured":"Kang, B., Xie, S., Rohrbach, M., Yan, Z., Gordo, A., Feng, J., & Kalantidis, Y. (2019). Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217"},{"key":"6326_CR28","unstructured":"Karimi, H., Derr, T., & Tang, J. (2019). Characterizing the decision boundary of deep neural networks. arXiv preprint arXiv:1912.11460"},{"key":"6326_CR29","unstructured":"Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., & Viegas, F. (2018). Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV). In International conference on machine learning (pp. 2668\u20132677). PMLR"},{"key":"6326_CR30","doi-asserted-by":"publisher","first-page":"81674","DOI":"10.1109\/ACCESS.2020.2991231","volume":"8","author":"B Kim","year":"2020","unstructured":"Kim, B., & Kim, J. (2020). Adjusting decision boundary for class imbalanced learning. IEEE Access, 8, 81674\u201381685.","journal-title":"IEEE Access"},{"key":"6326_CR31","doi-asserted-by":"publisher","first-page":"105662","DOI":"10.1016\/j.asoc.2019.105662","volume":"83","author":"G Kov\u00e1cs","year":"2019","unstructured":"Kov\u00e1cs, G. (2019). An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets. Applied Soft Computing, 83, 105662. https:\/\/doi.org\/10.1016\/j.asoc.2019.105662 (IF-2019=4.873)","journal-title":"Applied Soft Computing"},{"issue":"4","key":"6326_CR32","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","volume":"5","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk, B. (2016). Learning from imbalanced data: Open challenges and future directions. Progress in Artificial Intelligence, 5(4), 221\u2013232.","journal-title":"Progress in Artificial Intelligence"},{"key":"6326_CR33","unstructured":"Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images"},{"key":"6326_CR34","unstructured":"Li, B., Jin, J., Zhong, H., Hopcroft, J. E., & Wang, L. (2022). Why robust generalization in deep learning is difficult: Perspective of expressive power. arXiv preprint arXiv:2205.13863"},{"key":"6326_CR35","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Doll\u00e1r, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980\u20132988)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"1","key":"6326_CR36","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/e23010018","volume":"23","author":"P Linardatos","year":"2020","unstructured":"Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), 18.","journal-title":"Entropy"},{"key":"6326_CR37","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X. (2015). Deep learning face attributes in the wild. In Proceedings of international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2015.425"},{"key":"6326_CR38","unstructured":"Mickisch, D., Assion, F., Gre\u00dfner, F., G\u00fcnther, W., & Motta, M. (2020). Understanding the decision boundary of deep neural networks: An empirical study. arXiv preprint arXiv:2002.01810"},{"key":"6326_CR39","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., & Ng, A. Y. (2011). Reading digits in natural images with unsupervised feature learning."},{"issue":"11","key":"6326_CR40","doi-asserted-by":"publisher","first-page":"7","DOI":"10.23915\/distill.00007","volume":"2","author":"C Olah","year":"2017","unstructured":"Olah, C., Mordvintsev, A., & Schubert, L. (2017). Feature visualization. Distill, 2(11), 7.","journal-title":"Distill"},{"issue":"23","key":"6326_CR41","doi-asserted-by":"publisher","first-page":"3311","DOI":"10.1016\/S0042-6989(97)00169-7","volume":"37","author":"BA Olshausen","year":"1997","unstructured":"Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: A strategy employed by v1? Vision Research, 37(23), 3311\u20133325.","journal-title":"Vision Research"},{"key":"6326_CR42","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., & Lerer, A. (2017). Automatic differentiation in pytorch"},{"key":"6326_CR43","first-page":"1256","volume":"34","author":"M Pezeshki","year":"2021","unstructured":"Pezeshki, M., Kaba, O., Bengio, Y., Courville, A. C., Precup, D., & Lajoie, G. (2021). Gradient starvation: A learning proclivity in neural networks. Advances in Neural Information Processing Systems, 34, 1256\u20131272.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"6326_CR44","doi-asserted-by":"crossref","unstructured":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). \u201cWhy should i trust you?\u201d Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135\u20131144).","DOI":"10.1145\/2939672.2939778"},{"key":"6326_CR45","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision (pp. 618\u2013626).","DOI":"10.1109\/ICCV.2017.74"},{"key":"6326_CR46","first-page":"9573","volume":"33","author":"H Shah","year":"2020","unstructured":"Shah, H., Tamuly, K., Raghunathan, A., Jain, P., & Netrapalli, P. (2020). The pitfalls of simplicity bias in neural networks. Advances in Neural Information Processing Systems, 33, 9573\u20139585.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"6326_CR47","volume-title":"A value for n-person games","author":"LS Shapley","year":"1953","unstructured":"Shapley, L. S. (1953) A value for n-person games. RAND Corporation."},{"key":"6326_CR48","unstructured":"Shen, R., Bubeck, S., & Gunasekar, S. (2022). Data augmentation as feature manipulation. In International conference on machine learning (pp. 19773\u201319808). PMLR"},{"key":"6326_CR49","unstructured":"Simonyan, K., Vedaldi, A., & Zisserman, A. (2013). Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034"},{"key":"6326_CR50","unstructured":"Sundararajan, M., & Najmi, A. (2020). The many Shapley values for model explanation. In International conference on machine learning (pp. 9269\u20139278). PMLR"},{"key":"6326_CR51","unstructured":"Sundararajan, M., Taly, A., & Yan, Q. (2017). Axiomatic attribution for deep networks. In International conference on machine learning (pp. 3319\u20133328). PMLR"},{"key":"6326_CR52","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199"},{"key":"6326_CR53","doi-asserted-by":"crossref","unstructured":"Wang, H., Wu, X., Huang, Z., & Xing, E. P. (2020). High-frequency component helps explain the generalization of convolutional neural networks. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 8684\u20138694)","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"6326_CR54","unstructured":"Weiss, G. M., Provost, F. (2001). The effect of class distribution on classifier learning: An empirical study. Technical report, Rutgers University"},{"key":"6326_CR55","unstructured":"Ye, H.-J., Chen, H.-Y., Zhan, D.-C., & Chao, W.-L. (2020). Identifying and compensating for feature deviation in imbalanced deep learning. arXiv preprint arXiv:2001.01385"},{"key":"6326_CR56","doi-asserted-by":"crossref","unstructured":"Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818\u2013833). Springer","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"6326_CR57","unstructured":"Zhou, B., Cui, Q., Wei, X.-S., & Chen, Z.-M.: Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 9719\u20139728)"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06326-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-023-06326-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06326-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T14:44:36Z","timestamp":1717166676000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-023-06326-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,11]]},"references-count":57,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["6326"],"URL":"https:\/\/doi.org\/10.1007\/s10994-023-06326-9","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,11]]},"assertion":[{"value":"6 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2023","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 report no conflicts of interest or competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All authors consent to participate.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors consent to publication.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}