{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T22:09:45Z","timestamp":1740175785749,"version":"3.37.3"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T00:00:00Z","timestamp":1724025600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T00:00:00Z","timestamp":1724025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100005416","name":"Norwegian Research Council","doi-asserted-by":"crossref","award":["329745 Machine Teaching for Explainable AI"],"award-info":[{"award-number":["329745 Machine Teaching for Explainable AI"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"crossref"}]},{"name":"MINECO","award":["PID2021-122830OB-C42"],"award-info":[{"award-number":["PID2021-122830OB-C42"]}]},{"DOI":"10.13039\/501100003359","name":"Generalitat Valenciana","doi-asserted-by":"publisher","award":["CIPROM\/2022\/6"],"award-info":[{"award-number":["CIPROM\/2022\/6"]}],"id":[{"id":"10.13039\/501100003359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["No. 952215 (TAILOR)"],"award-info":[{"award-number":["No. 952215 (TAILOR)"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) de la Comunitat Valenciana 2014-2020"],"award-info":[{"award-number":["Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) de la Comunitat Valenciana 2014-2020"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006502","name":"Defense Sciences Office, DARPA","doi-asserted-by":"publisher","award":["HR00112120007 (RECoG-AI)"],"award-info":[{"award-number":["HR00112120007 (RECoG-AI)"]}],"id":[{"id":"10.13039\/100006502","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017561","name":"Future of Life Institute","doi-asserted-by":"publisher","award":["RFP2-152"],"award-info":[{"award-number":["RFP2-152"]}],"id":[{"id":"10.13039\/100017561","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["No. 952215 (TAILOR)"],"award-info":[{"award-number":["No. 952215 (TAILOR)"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s40747-024-01599-6","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T09:02:59Z","timestamp":1724058179000},"page":"8287-8317","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A general supply-inspect cost framework to regulate the reliability-usability trade-offs for few-shot inference"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2902-6477","authenticated-orcid":false,"given":"Fernando","family":"Mart\u00ednez-Plumed","sequence":"first","affiliation":[]},{"given":"Gonzalo","family":"Jaimovitch-L\u00f3pez","sequence":"additional","affiliation":[]},{"given":"C\u00e8sar","family":"Ferri","sequence":"additional","affiliation":[]},{"given":"Mar\u00eda Jos\u00e9","family":"Ram\u00edrez-Quintana","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Hern\u00e1ndez-Orallo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,19]]},"reference":[{"issue":"3","key":"1599_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv (CSUR) 53(3):1\u201334","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"11","key":"1599_CR2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ac8368","volume":"33","author":"H Tao","year":"2022","unstructured":"Tao H, Cheng L, Qiu J, Stojanovic V (2022) Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic. Measur Sci Technol 33(11):115005","journal-title":"Measur Sci Technol"},{"key":"1599_CR3","doi-asserted-by":"crossref","unstructured":"Wang K, Liew JH, Zou Y, Zhou D, Feng J (2019) Panet: Few-shot image semantic segmentation with prototype alignment. In: proceedings of the IEEE\/CVF international conference on computer vision. p. 9197\u20139206","DOI":"10.1109\/ICCV.2019.00929"},{"key":"1599_CR4","doi-asserted-by":"crossref","unstructured":"Yang B, Liu C, Li B, Jiao J, Ye Q (2020) Prototype mixture models for few-shot semantic segmentation. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part VIII 16. Springer. p. 763\u2013778","DOI":"10.1007\/978-3-030-58598-3_45"},{"key":"1599_CR5","unstructured":"Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et\u00a0al. (2020) Language Models are Few-Shot Learners. In: Advances in Neural Information Processing Systems. p. 1877\u20131901"},{"key":"1599_CR6","unstructured":"OpenAI. GPT-4 technical report. ArXiv. 2023;abs\/2303.08774"},{"key":"1599_CR7","unstructured":"Zeng W, Ren X, Su T, Wang H, Liao Y, Wang Z, et\u00a0al (2021) PanGu-$$\\alpha $$: large-scale autoregressive pretrained chinese language models with auto-parallel computation. arXiv preprint arXiv:2104.12369"},{"key":"1599_CR8","unstructured":"Chowdhery A, et al (2022) PaLM: scaling language modeling with pathways. arXiv:2204.02311 [cs]"},{"key":"1599_CR9","doi-asserted-by":"publisher","unstructured":"BigScience, et\u00a0al (2023) BLOOM: A 176B-parameter open-access multilingual language model. https:\/\/doi.org\/10.48550\/arXiv.2211.05100. arXiv:2211.05100 [cs]","DOI":"10.48550\/arXiv.2211.05100"},{"key":"1599_CR10","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, et\u00a0al (2023) Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971"},{"key":"1599_CR11","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1613\/jair.1.14157","volume":"77","author":"W Schellaert","year":"2023","unstructured":"Schellaert W, Mart\u00ednez-Plumed F, Vold K, Burden J, Casares PA, Loe BS et al (2023) Your prompt is my command: on assessing the human-centred generality of multimodal models. J Artif Intell Res 77:377\u2013394","journal-title":"J Artif Intell Res"},{"issue":"11","key":"1599_CR12","first-page":"1","volume":"24","author":"V Franc","year":"2023","unstructured":"Franc V, Prusa D, Voracek V (2023) Optimal strategies for reject option classifiers. J Mach Learn Res 24(11):1\u201349","journal-title":"J Mach Learn Res"},{"key":"1599_CR13","doi-asserted-by":"crossref","unstructured":"Pugnana A, Ruggieri S (2023) A model-agnostic heuristics for selective classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a037. p. 9461\u20139469","DOI":"10.1609\/aaai.v37i8.26133"},{"key":"1599_CR14","doi-asserted-by":"crossref","unstructured":"Hendrickx K, Perini L, Van\u00a0der Plas D, Meert W, Davis J (2023) Machine learning with a reject option: a survey. arXiv preprint arXiv:2107.11277","DOI":"10.1007\/s10994-024-06534-x"},{"key":"1599_CR15","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s11390-020-9487-4","volume":"35","author":"P Kumar","year":"2020","unstructured":"Kumar P, Gupta A (2020) Active learning query strategies for classification, regression, and clustering: a survey. J Comput Sci Technol 35:913\u2013945","journal-title":"J Comput Sci Technol"},{"key":"1599_CR16","unstructured":"Rattenbury T, Hellerstein JM, Heer J, Kandel S, Carreras C (2017) Principles of data wrangling: practical techniques for data preparation. O\u2019Reilly Media, Inc"},{"issue":"6","key":"1599_CR17","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1007\/s10994-022-06259-9","volume":"112","author":"G Jaimovitch-L\u00f3pez","year":"2023","unstructured":"Jaimovitch-L\u00f3pez G, Ferri C, Hern\u00e1ndez-Orallo J, Mart\u00ednez-Plumed F, Ram\u00edrez-Quintana MJ (2023) Can language models automate data wrangling? Mach Learn 112(6):2053\u20132082","journal-title":"Mach Learn"},{"key":"1599_CR18","unstructured":"Charoenphakdee N, Cui Z, Zhang Y, Sugiyama M (2021) Classification with rejection based on cost-sensitive classification. In: International Conference on Machine Learning. PMLR. p. 1507\u20131517"},{"key":"1599_CR19","unstructured":"Zhou L, Mart\u00ednez-Plumed F, Hern\u00e1ndez-Orallo J, Ferri C, Schellaert W (2022) Reject before you run: small assessors anticipate big language models. In: 1st AI Evaluation Beyond Metrics Workshop (EBEM), CEUR Proceedings, volume 3169"},{"key":"1599_CR20","doi-asserted-by":"crossref","unstructured":"Lu Y, Bartolo M, Moore A, Riedel S, Stenetorp P (2021) Fantastically ordered prompts and where to find them: overcoming few-shot prompt order sensitivity. arXiv preprint arXiv:2104.08786","DOI":"10.18653\/v1\/2022.acl-long.556"},{"key":"1599_CR21","doi-asserted-by":"crossref","unstructured":"Flach PA (2016) ROC analysis. In: Encyclopedia of Machine Learning and Data Mining. Springer, p. 1\u20138","DOI":"10.1007\/978-1-4899-7502-7_739-1"},{"key":"1599_CR22","doi-asserted-by":"crossref","unstructured":"Nakas C, Bantis L, Gatsonis C (2023) ROC analysis for classification and prediction in practice. CRC Press","DOI":"10.1201\/9780429170140"},{"issue":"8","key":"1599_CR23","first-page":"1","volume":"54","author":"Y Tian","year":"2021","unstructured":"Tian Y, Si L, Zhang X, Cheng R, He C, Tan KC et al (2021) Evolutionary large-scale multi-objective optimization: a survey. ACM Comput Surv (CSUR) 54(8):1\u201334","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1599_CR24","unstructured":"Contreras-Ochando L, Ferri C, Hern\u00e1ndez-Orallo J, Mart\u00ednez-Plumed F, Ram\u00edrez-Quintana MJ, Katayama S (2019) Automated data transformation with inductive programming and dynamic background knowledge. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019. Springer, p. 735\u2013751"},{"key":"1599_CR25","unstructured":"Contreras-Ochando L, Ferri C, Hern\u00e1ndez-Orallo J, Mart\u00ednez-Plumed F, Ram\u00edrez-Quintana MJ, Katayama S (2019) BK-ADAPT: dynamic background knowledge for automating data transformation. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer. p. 755\u2013759"},{"key":"1599_CR26","unstructured":"Srivastava A, Rastogi A, Rao A, Shoeb AAM, Abid A, Fisch A, et\u00a0al. (2022) Beyond the imitation game: In: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615"},{"issue":"6641","key":"1599_CR27","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1126\/science.adf6369","volume":"380","author":"R Burnell","year":"2023","unstructured":"Burnell R, Schellaert W, Burden J, Ullman TD, Martinez-Plumed F, Tenenbaum JB et al (2023) Rethink reporting of evaluation results in AI. Science 380(6641):136\u2013138","journal-title":"Science"},{"key":"1599_CR28","doi-asserted-by":"crossref","unstructured":"Virani N, Iyer N, Yang Z (2020) Justification-based reliability in machine learning. In: Proc. of the AAAI Conf. on Artificial Intelligence. vol.\u00a034. p. 6078\u20136085","DOI":"10.1609\/aaai.v34i04.6071"},{"key":"1599_CR29","doi-asserted-by":"publisher","unstructured":"Cabitza F, Campagner A, Balsano C (2020) Bridging the \u201clast mile\u201d gap between AI implementation and operation:\u201cdata awareness\u201d that matters. Ann Transl Med 8(7). https:\/\/doi.org\/10.21037\/atm.2020.03.63","DOI":"10.21037\/atm.2020.03.63"},{"key":"1599_CR30","doi-asserted-by":"crossref","unstructured":"De A, Koley P, Ganguly N, Gomez-Rodriguez M (2020) Regression under human assistance. In: Proc. of the AAAI Conf. on Artificial Intelligence. vol.\u00a034. p. 2611\u20132620","DOI":"10.1609\/aaai.v34i03.5645"},{"key":"1599_CR31","doi-asserted-by":"publisher","first-page":"36322","DOI":"10.1109\/ACCESS.2019.2905015","volume":"7","author":"Z Pan","year":"2019","unstructured":"Pan Z, Yu W, Yi X, Khan A, Yuan F, Zheng Y (2019) Recent progress on generative adversarial networks (GANs): a survey. IEEE Access 7:36322\u201336333","journal-title":"IEEE Access"},{"key":"1599_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100285","volume":"38","author":"G Harshvardhan","year":"2020","unstructured":"Harshvardhan G, Gourisaria MK, Pandey M, Rautaray SS (2020) A comprehensive survey and analysis of generative models in machine learning. Comput Sci Rev 38:100285","journal-title":"Comput Sci Rev"},{"issue":"3","key":"1599_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3446374","volume":"54","author":"D Saxena","year":"2021","unstructured":"Saxena D, Cao J (2021) Generative adversarial networks (GANs) challenges, solutions, and future directions. ACM Comput Surv (CSUR) 54(3):1\u201342","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1599_CR34","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: relation network for few-shot learning. In: Proc. of the IEEE Conf. on computer vision and pattern recognition. p. 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"1599_CR35","doi-asserted-by":"crossref","unstructured":"Xu S, Semnani S, Campagna G, Lam M (2020) AutoQA: from databases to Q &A semantic parsers with only synthetic training data. In: Proc. of the 2020 Conf. on Empirical Methods in Natural Language Processing (EMNLP). p. 422\u2013434","DOI":"10.18653\/v1\/2020.emnlp-main.31"},{"key":"1599_CR36","doi-asserted-by":"crossref","unstructured":"Izacard G, Grave E (2020) Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282","DOI":"10.18653\/v1\/2021.eacl-main.74"},{"key":"1599_CR37","unstructured":"Hendrycks D, Burns C, Basart S, Zou A, Mazeika M, Song D, et\u00a0al (2020) Measuring massive multitask language understanding. In: International Conf. on Learning Representations"},{"key":"1599_CR38","doi-asserted-by":"crossref","unstructured":"Reynolds L, McDonell K (2021) Prompt programming for large language models: beyond the few-shot paradigm. arXiv preprint arXiv:2102.07350","DOI":"10.1145\/3411763.3451760"},{"key":"1599_CR39","unstructured":"Scao TL, Rush AM (2021) How many data points is a prompt worth? arXiv preprint arXiv:2103.08493"},{"key":"1599_CR40","unstructured":"Bommasani R, Hudson DA, Adeli E, Altman R, Arora S, von Arx S, et\u00a0al. (2021) On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258"},{"issue":"1","key":"1599_CR41","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TIT.1970.1054406","volume":"16","author":"C Chow","year":"1970","unstructured":"Chow C (1970) On optimum recognition error and reject tradeoff. IEEE Trans Inform Theory 16(1):41\u201346","journal-title":"IEEE Trans Inform Theory"},{"key":"1599_CR42","doi-asserted-by":"crossref","unstructured":"Herbei R, Wegkamp MH (2006) Classification with reject option. In: Canadian Journal of Statistics\/La Revue Canadienne de Statistique. p. 709\u2013721","DOI":"10.1002\/cjs.5550340410"},{"issue":"59","key":"1599_CR43","first-page":"1823","volume":"9","author":"PL Bartlett","year":"2008","unstructured":"Bartlett PL, Wegkamp MH (2008) Classification with a reject option using a hinge loss. J Mach Learn Res 9(59):1823\u20131840","journal-title":"J Mach Learn Res"},{"issue":"4","key":"1599_CR44","doi-asserted-by":"publisher","first-page":"1368","DOI":"10.3150\/10-BEJ320","volume":"17","author":"M Wegkamp","year":"2011","unstructured":"Wegkamp M, Yuan M (2011) Support vector machines with a reject option. Bernoulli 17(4):1368\u20131385","journal-title":"Bernoulli"},{"key":"1599_CR45","unstructured":"Denis C, Hebiri M, Zaoui A (2020) Regression with reject option and application to kNN. arXiv preprint arXiv:2006.16597"},{"key":"1599_CR46","unstructured":"Lee JK, Bu Y, Rajan D, Sattigeri P, Panda R, Das S, et\u00a0al (2021) Fair selective classification via sufficiency. In: International Conf. on Machine Learning. PMLR. p. 6076\u20136086"},{"key":"1599_CR47","doi-asserted-by":"crossref","unstructured":"Tortorella F (2000) An optimal reject rule for binary classifiers. In: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). Springer. p. 611\u2013620","DOI":"10.1007\/3-540-44522-6_63"},{"issue":"2","key":"1599_CR48","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10994-007-5013-y","volume":"68","author":"T Pietraszek","year":"2007","unstructured":"Pietraszek T (2007) On the use of ROC analysis for the optimization of abstaining classifiers. Mach Learn 68(2):137\u2013169","journal-title":"Mach Learn"},{"key":"1599_CR49","unstructured":"Settles B (2011) From theories to queries: active learning in practice. In: Active Learning and Experimental Design workshop In conjunction with AISTATS 2010. JMLR Workshop and Conf. Proc. p. 1\u201318"},{"key":"1599_CR50","unstructured":"Chen X, Price E (2019) Active regression via linear-sample sparsification. In: Beygelzimer A, Hsu D, (eds) Proc. of the Thirty-Second Conf. on Learning Theory. vol.\u00a099 of Proc. of Machine Learning Research. PMLR. p. 663\u2013695"},{"key":"1599_CR51","unstructured":"Margineantu DD (2005) Active cost-sensitive learning. In: Proc. of the 19th International Joint Conf. on Artificial Intelligence. p. 1622\u20131623"},{"key":"1599_CR52","unstructured":"Settles B, Craven M, Friedland L (2008) Active learning with real annotation costs. In: Proc. of the NIPS workshop on cost-sensitive learning. vol.\u00a01. Available at https:\/\/api.semanticscholar.org\/CorpusID:16285026"},{"key":"1599_CR53","unstructured":"Haertel RA, Seppi KD, Ringger EK, Carroll JL (2008) Return on investment for active learning. In: Proc. of the NIPS Workshop on cost-sensitive learning. vol.\u00a072"},{"key":"1599_CR54","doi-asserted-by":"crossref","unstructured":"Culotta A, McCallum A (2005) Reducing labeling effort for structured prediction tasks. In: Proc. of the AAAI Conf. on Artificial Intelligence,. vol.\u00a05. p. 746\u2013751","DOI":"10.21236\/ADA440382"},{"issue":"2","key":"1599_CR55","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10115-012-0507-8","volume":"35","author":"Y Fu","year":"2013","unstructured":"Fu Y, Zhu X, Li B (2013) A survey on instance selection for active learning. Knowl Inform Syst 35(2):249\u2013283","journal-title":"Knowl Inform Syst"},{"issue":"2","key":"1599_CR56","first-page":"255","volume":"13","author":"R El-Yaniv","year":"2012","unstructured":"El-Yaniv R, Wiener Y (2012) Active learning via perfect selective classification. J Mach Learn Res 13(2):255\u2013279","journal-title":"J Mach Learn Res"},{"key":"1599_CR57","doi-asserted-by":"crossref","unstructured":"Shah K, Manwani N (2020) Online active learning of reject option classifiers. In: Proc. of the AAAI Conf. on Artificial Intelligence. vol.\u00a034. p. 5652\u20135659","DOI":"10.1609\/aaai.v34i04.6019"},{"issue":"18","key":"1599_CR58","doi-asserted-by":"publisher","first-page":"10139","DOI":"10.1002\/rnc.6354","volume":"32","author":"C Zhou","year":"2022","unstructured":"Zhou C, Tao H, Chen Y, Stojanovic V, Paszke W (2022) Robust point-to-point iterative learning control for constrained systems: A minimum energy approach. Int J Robust Nonlinear Control 32(18):10139\u201310161","journal-title":"Int J Robust Nonlinear Control"},{"issue":"Oct","key":"1599_CR59","first-page":"2813","volume":"13","author":"J Hern\u00e1ndez-Orallo","year":"2012","unstructured":"Hern\u00e1ndez-Orallo J, Flach P, Ferri C (2012) A unified view of performance metrics: translating threshold choice into expected classification loss. J Mach Learn Res 13(Oct):2813\u20132869","journal-title":"J Mach Learn Res"},{"key":"1599_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdeveco.2022.102992","volume":"161","author":"D Jeong","year":"2023","unstructured":"Jeong D, Aggarwal S, Robinson J, Kumar N, Spearot A, Park DS (2023) Exhaustive or exhausting? Evidence on respondent fatigue in long surveys. J Dev Econ 161:102992","journal-title":"J Dev Econ"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01599-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01599-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01599-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T22:20:14Z","timestamp":1729117214000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01599-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,19]]},"references-count":60,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1599"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01599-6","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2024,8,19]]},"assertion":[{"value":"31 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 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":"On behalf of all authors, the corresponding author states that there is no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}