{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:38:02Z","timestamp":1783021082104,"version":"3.54.6"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032045546","type":"print"},{"value":"9783032045553","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04555-3_10","type":"book-chapter","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T08:56:19Z","timestamp":1757580979000},"page":"115-127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Conformalized Causal Learning for\u00a0Uncertainty-Aware Mineral Prospectivity Mapping"],"prefix":"10.1007","author":[{"given":"Evelyn Jessica","family":"Jaya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinying","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinbing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nanyang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.resourpol.2023.104263","volume":"86","author":"Z An","year":"2023","unstructured":"An, Z., Zhao, Y., Zhang, Y.: Mineral exploration and the green transition: opportunities and challenges for the mining industry. Resour. Policy 86, 104263 (2023)","journal-title":"Resour. Policy"},{"key":"10_CR2","unstructured":"D.G.G.S. Staff: Alaska merged geophysical data grids. Alaska Division of Geological & Geophysical Surveys (2016)"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Ding, Y., Liu, J., Xiong, J., Shi, Y.: Revisiting the evaluation of uncertainty estimation and its application to explore model complexity-uncertainty trade-off. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp.\u00a04\u20135 (2020)","DOI":"10.1109\/CVPRW50498.2020.00010"},{"issue":"1\u20132","key":"10_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0169-1368(01)00016-6","volume":"18","author":"RJ Goldfarb","year":"2001","unstructured":"Goldfarb, R.J., Groves, D.I., Gardoll, S.: Orogenic gold and geologic time: a global synthesis. Ore Geol. Rev. 18(1\u20132), 1\u201375 (2001)","journal-title":"Ore Geol. Rev."},{"issue":"1","key":"10_CR5","first-page":"1","volume":"98","author":"DI Groves","year":"2003","unstructured":"Groves, D.I., Goldfarb, R.J., Robert, F., Hart, C.J.: Gold deposits in metamorphic belts: overview of current understanding, outstanding problems, future research, and exploration significance. Econ. Geol. 98(1), 1\u201329 (2003)","journal-title":"Econ. Geol."},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1016\/j.oregeorev.2015.01.004","volume":"71","author":"J Harris","year":"2015","unstructured":"Harris, J., Grunsky, E., Behnia, P., Corrigan, D.: Data-and knowledge-driven mineral prospectivity maps for Canada\u2019s north. Ore Geol. Rev. 71, 788\u2013803 (2015)","journal-title":"Ore Geol. Rev."},{"key":"10_CR7","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.cageo.2015.06.006","volume":"82","author":"SA Hosseini","year":"2015","unstructured":"Hosseini, S.A., Abedi, M.: Data envelopment analysis: a knowledge-driven method for mineral prospectivity mapping. Comput. Geosci. 82, 111\u2013119 (2015)","journal-title":"Comput. Geosci."},{"key":"10_CR8","unstructured":"Idaho Geological Survey Staff: Idaho geological survey. Online (2022). https:\/\/www.idahogeology.org"},{"key":"10_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.oregeorev.2023.105381","volume":"156","author":"Q Li","year":"2023","unstructured":"Li, Q., Chen, G., Luo, L.: Mineral prospectivity mapping using attention-based convolutional neural network. Ore Geol. Rev. 156, 105381 (2023)","journal-title":"Ore Geol. Rev."},{"issue":"20","key":"10_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-020-06062-7","volume":"13","author":"MA Mahboob","year":"2020","unstructured":"Mahboob, M.A., Celik, T., Genc, B.: Predictive modeling and comparative evaluation of geostatistical models for geochemical exploration through stream sediments. Arab. J. Geosci. 13(20), 1\u201321 (2020)","journal-title":"Arab. J. Geosci."},{"issue":"7","key":"10_CR11","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1007\/s11004-022-10038-6","volume":"55","author":"M Parsa","year":"2023","unstructured":"Parsa, M., Harris, J., Sherlock, R.: Improving mineral prospectivity model generalization: an example from orogenic gold mineralization of the sturgeon lake transect, ontario, canada. Math. Geosci. 55(7), 943\u2013961 (2023)","journal-title":"Math. Geosci."},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.oregeorev.2014.09.031","volume":"65","author":"GN Phillips","year":"2015","unstructured":"Phillips, G.N., Powell, R.: Hydrothermal alteration in the witwatersrand goldfields. Ore Geol. Rev. 65, 245\u2013273 (2015)","journal-title":"Ore Geol. Rev."},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Rama, B., Praveen, P., Sinha, H., Choudhury, T.: A study on causal rule discovery with pc algorithm. In: 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions)(ICTUS), pp. 616\u2013621. IEEE (2017)","DOI":"10.1109\/ICTUS.2017.8286083"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Sadeghi, B., Grunsky, E., Pawlowsky-Glahn, V.: Uncertainty quantification. In: Encyclopedia of Mathematical Geosciences, pp. 1583\u20131589. Springer (2023)","DOI":"10.1007\/978-3-030-85040-1_334"},{"key":"10_CR15","unstructured":"Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. 9(3) (2008)"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Syrgkanis, V., et\u00a0al.: Causal inference and machine learning in practice with EconML and CausalML: industrial use cases at Microsoft, TripAdvisor, Uber. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 4072\u20134073 (2021)","DOI":"10.1145\/3447548.3470792"},{"key":"10_CR17","unstructured":"U.S. Geological Survey Staff: U.s. geological survey. Online (2022). https:\/\/www.usgs.gov"},{"issue":"1","key":"10_CR18","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/s40537-024-00905-w","volume":"11","author":"H Wang","year":"2024","unstructured":"Wang, H., Liang, Q., Hancock, J.T., Khoshgoftaar, T.M.: Feature selection strategies: a comparative analysis of shap-value and importance-based methods. J. Big Data 11(1), 44 (2024)","journal-title":"J. Big Data"},{"key":"10_CR19","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s11053-019-09510-8","volume":"29","author":"J Wang","year":"2020","unstructured":"Wang, J., Zuo, R., Xiong, Y.: Mapping mineral prospectivity via semi-supervised random forest. Nat. Resour. Res. 29, 189\u2013202 (2020)","journal-title":"Nat. Resour. Res."},{"key":"10_CR20","unstructured":"Wellmer, F.W.: Statistical evaluations in exploration for mineral deposits. Springer Science & Business Media (2012)"},{"key":"10_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2020.104484","volume":"140","author":"Y Xiong","year":"2020","unstructured":"Xiong, Y., Zuo, R.: Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine. Comput. Geosci. 140, 104484 (2020)","journal-title":"Comput. Geosci."},{"key":"10_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2022.105075","volume":"161","author":"N Yang","year":"2022","unstructured":"Yang, N., Zhang, Z., Yang, J., Hong, Z.: Applications of data augmentation in mineral prospectivity prediction based on convolutional neural networks. Comput. Geosci. 161, 105075 (2022)","journal-title":"Comput. Geosci."},{"issue":"1","key":"10_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40649-019-0069-y","volume":"6","author":"S Zhang","year":"2019","unstructured":"Zhang, S., Tong, H., Xu, J., Maciejewski, R.: Graph convolutional networks: a comprehensive review. Comput. Soc. Netw. 6(1), 1\u201323 (2019)","journal-title":"Comput. Soc. Netw."},{"issue":"9","key":"10_CR24","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1007\/s11430-024-1309-9","volume":"67","author":"R Zuo","year":"2024","unstructured":"Zuo, R., et al.: Explainable artificial intelligence models for mineral prospectivity mapping. Sci. China Earth Sci. 67(9), 2864\u20132875 (2024)","journal-title":"Sci. China Earth Sci."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04555-3_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:47:59Z","timestamp":1783018079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04555-3_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,12]]},"ISBN":["9783032045546","9783032045553"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04555-3_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,12]]},"assertion":[{"value":"12 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}