{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T21:09:15Z","timestamp":1779916155396,"version":"3.53.1"},"reference-count":35,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:00:00Z","timestamp":1722384000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the High-level talent introduction program for the Guizhou Institute Of Technology","award":["0203001018040"],"award-info":[{"award-number":["0203001018040"]}]},{"name":"the High-level talent introduction program for the Guizhou Institute Of Technology","award":["0203001018040"],"award-info":[{"award-number":["0203001018040"]}]},{"name":"Guizhou Science and technology innovation talent team project","award":["CXTD[2023]008"],"award-info":[{"award-number":["CXTD[2023]008"]}]},{"name":"Special Projects and Research Topics of Guizhou Association for Science and Technology","award":["0201011022001"],"award-info":[{"award-number":["0201011022001"]}]},{"name":"Guizhou Science and technology innovation talent team project","award":["CXTD[2023]008"],"award-info":[{"award-number":["CXTD[2023]008"]}]},{"name":"Guizhou Science and technology innovation talent team project","award":["CXTD[2023]008"],"award-info":[{"award-number":["CXTD[2023]008"]}]},{"name":"Guizhou Science and technology innovation talent team project","award":["CXTD[2023]008"],"award-info":[{"award-number":["CXTD[2023]008"]}]},{"name":"the High-level talent introduction program for the Guizhou Institute Of Technology","award":["0203001018040"],"award-info":[{"award-number":["0203001018040"]}]},{"name":"the High-level talent introduction program for the Guizhou Institute Of Technology","award":["0203001018040"],"award-info":[{"award-number":["0203001018040"]}]},{"name":"Special Projects and Research Topics of Guizhou Association for Science and Technology","award":["0201011022001"],"award-info":[{"award-number":["0201011022001"]}]},{"name":"Special Projects and Research Topics of Guizhou Association for Science and Technology","award":["0201011022001"],"award-info":[{"award-number":["0201011022001"]}]},{"name":"Special Projects and Research Topics of Guizhou Association for Science and Technology","award":["0201011022001"],"award-info":[{"award-number":["0201011022001"]}]},{"name":"Special and research project of Guizhou Association for Science and Technology","award":["Research on the Exploration Practice and Selection Theory of Lithium Resources in Northern Guizhou under the Target of Carbon Peak and Carbon Neutrality."],"award-info":[{"award-number":["Research on the Exploration Practice and Selection Theory of Lithium Resources in Northern Guizhou under the Target of Carbon Peak and Carbon Neutrality."]}]},{"name":"Special and research project of Guizhou Association for Science and Technology","award":["Research on the Exploration Practice and Selection Theory of Lithium Resources in Northern Guizhou under the Target of Carbon Peak and Carbon Neutrality."],"award-info":[{"award-number":["Research on the Exploration Practice and Selection Theory of Lithium Resources in Northern Guizhou under the Target of Carbon Peak and Carbon Neutrality."]}]},{"name":"Special and research project of Guizhou Association for Science and Technology","award":["Research on the Exploration Practice and Selection Theory of Lithium Resources in Northern Guizhou under the Target of Carbon Peak and Carbon Neutrality."],"award-info":[{"award-number":["Research on the Exploration Practice and Selection Theory of Lithium Resources in Northern Guizhou under the Target of Carbon Peak and Carbon Neutrality."]}]},{"name":"Special and research project of Guizhou Association for Science and Technology","award":["Research on the Exploration Practice and Selection Theory of Lithium Resources in Northern Guizhou under the Target of Carbon Peak and Carbon Neutrality."],"award-info":[{"award-number":["Research on the Exploration Practice and Selection Theory of Lithium Resources in Northern Guizhou under the Target of Carbon Peak and Carbon Neutrality."]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Wu-Zheng-Dao District in China is the world\u2019s most famous mining areas. It hosts several world-class deposits, such as Xinming, Datang and Luolong bauxite deposits. Although this area still has significant potential for the discovery of new deposits, mineral prediction has become increasingly diffcult as the number of shallow deposits diminishes. Therefore, it is necessary to explore new and effective metallogenic prediction methods.Weights of evidence and machine-learning algorithms were used for mineral prospecting in this study. This study used a confusion matrix, receiver operating characteristic (ROC) curve,and prediction efficiency curve to evaluate the prediction results of each machine algorithm. The results showed that 95.9% of the deposits were located in high and distant scenic areas, accounting for 10% of the total area.The prospectivity map of the Wu-Zheng-Dao district shows that the high prospective areas are generally confined to the claystone and carbonatite rocks of the Eastern region, in particular, of the clay layers, and several areas of high prospectivity also occur in the Southern Cross Domain. According to the predicted results, after on-site exploration, design, and construction, Yanfengqian bauxite deposit was discovered, with an average thickness of 1.82 meters; The average content of Al<jats:sub>2<\/jats:sub>O<jats:sub>3<\/jats:sub> is 61.24%; The resource amount is 28.9503 million tons.<\/jats:p>","DOI":"10.1371\/journal.pone.0305917","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T19:52:06Z","timestamp":1722455526000},"page":"e0305917","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction and practical application of bauxite mineralization in Wuzhengdao area, Guizhou, China"],"prefix":"10.1371","volume":"19","author":[{"given":"Shiqiang","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5634-1013","authenticated-orcid":true,"given":"Wu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"key":"pone.0305917.ref001","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.cageo.2011.12.014","article-title":"Support vector machine for multi-classification of mineral prospectivity areas.","volume":"46","author":"M Abedi","year":"2012","journal-title":"Computers & Geosciences"},{"key":"pone.0305917.ref002","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11053-014-9250-z","article-title":"Data-Driven Evidential Belief Modeling of Mineral Potential Using Few Prospects and Evidence with Missing Values","volume":"24","author":"M. Carranza E J","year":"2015","journal-title":"Natural Resources Research"},{"key":"pone.0305917.ref003","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.cageo.2014.10.004","article-title":"Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines).","volume":"74","author":"M Carranza E J","year":"2015","journal-title":"Computers & Geosciences"},{"key":"pone.0305917.ref004","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1080\/08120090500432470","article-title":"Evolution of the Isan Orogeny at the southeastern margin of the Mt Isa Inlier","volume":"53","author":"D Giles","year":"2006","journal-title":"Australian Journal of Earth Sciences"},{"key":"pone.0305917.ref005","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1016\/j.energy.2018.01.093","article-title":"A Novel Evolution Tree for Analyzing the Global Energy Consumption Structure.","volume":"147","author":"Y. Hu","year":"2018","journal-title":"Energy"},{"key":"pone.0305917.ref006","article-title":"Mitigating tribological challenges in machining additively manufactured stainless steel with cryogenic-MQL hybrid technology[J]","volume":"193109343","author":"N Ross S","year":"2024","journal-title":"Tribology International"},{"key":"pone.0305917.ref007","article-title":"Parallel structure of crayfish optimization with arithmetic optimization for classifying the friction behaviour of Ti-6Al-4V alloy for complex machinery applications[J].","volume":"286111389","author":"S Chauhan","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"pone.0305917.ref008","article-title":"A synergy of an evolutionary algorithm with slime mould algorithm through series and parallel construction for improving global optimization and conventional design problem[J]","volume":"118","author":"C Sumika","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"1","key":"pone.0305917.ref009","first-page":"1","article-title":"Pelton Wheel Bucket Fault Diagnosis Using Improved Shannon Entropy and Expectation Maximization Principal Component Analysis [J]","volume":"10","author":"Govind Vashishtha","year":"2021","journal-title":"Journal of Vibration Engineering Technologies,"},{"key":"pone.0305917.ref010","article-title":"An adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis[J].","volume":"226114191","author":"S Chauhan","year":"2024","journal-title":"Measurement"},{"key":"pone.0305917.ref011","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/S0921-8009(03)00188-5","article-title":"STIRPAT, IPAT and ImPACT: Analytic Tools for Unpacking the Driving Forces of Environmental Impacts.","volume":"46","author":"R. York","year":"2003","journal-title":"Ecol. Econ"},{"key":"pone.0305917.ref012","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.eiar.2005.11.007","article-title":"Analyzing Impact Factors of CO2 Emissions Using the STIRPAT Model. Environ","volume":"26","author":"Y. Fan","year":"2006","journal-title":"Impact Assess. Rev"},{"key":"pone.0305917.ref013","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.eneco.2009.09.016","article-title":"Supply-Side Structural Effect on Carbon Emissions in China","volume":"32","author":"Y. Zhang","year":"2010","journal-title":"Energy Econ."},{"key":"pone.0305917.ref014","first-page":"12","article-title":"Why adults participate? J","volume":"30","author":"N. Emmalou Van Tilburg","year":"1992","journal-title":"Ext"},{"key":"pone.0305917.ref015","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1080\/02601370.2021.1882594","article-title":"Inquiring into lifelong learning intention: Comparisons of gender, employment status, and media exposure. Int.","volume":"40","author":"M. Thongmak","year":"2021","journal-title":"J.Lifelong Educ"},{"key":"pone.0305917.ref016","first-page":"7","author":"U. Bronfenbrenner","year":"1979","journal-title":"The Ecology of Human Development; Harvard University Press: Cambridge, MA, USA,"},{"key":"pone.0305917.ref017","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1037\/0012-1649.22.6.723","article-title":"Ecology of the family as a context for human development: Research perspectives","volume":"22","author":"U. Bronfenbrenner","year":"1986","journal-title":"Dev. Psychol"},{"key":"pone.0305917.ref018","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1037\/10176-018","volume-title":"Examining Lives in Context:Perspectives on the Ecology of Human Development;","author":"U. Bronfenbrenner","year":"1995"},{"key":"pone.0305917.ref019","first-page":"7365","article-title":"Predicting the variables that determine university (re-) entrance as a career development using support vectormachines with recursive feature elimination","volume":"12","author":"T. Park","year":"2020","journal-title":"The case of South Korea. Sustainability"},{"key":"pone.0305917.ref020","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"F. Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res"},{"key":"pone.0305917.ref021","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.scitotenv.2016.08.190","article-title":"Response of microbial community structure and function toshort-term biochar amendment in an intensively managed bamboo (Phyllostachys praecox) plantation soil: Effect of particle sizeand addition rate.","volume":"574","author":"J. Chen","year":"2017","journal-title":"Sci. Total Environ"},{"key":"pone.0305917.ref022","first-page":"46","article-title":"Predicting Highway Concrete Pavement Damage using XGBoost. Korean J. Constr","volume":"21","author":"Y. Lee","year":"2020","journal-title":"Eng. Manag"},{"key":"pone.0305917.ref023","article-title":"Complete Machine Learning Guide to Parameter Tuning in Gradient Boosting (GBM) in Python","author":"A. Jain","year":"2016","journal-title":"Anal. Vidhya"},{"key":"pone.0305917.ref024","doi-asserted-by":"crossref","unstructured":"Chen, T.; Guestrin, C Xgboost: A scalable tree boosting system. In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13\u201317 August 2016; pp. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"pone.0305917.ref025","first-page":"11","article-title":"Educational data mining: Prediction of students\u2019 academic performance using machine learning algorithms.","volume":"9","author":"M Ya \u02d8gc\u0131","year":"2022","journal-title":"SmartLearn. Environ"},{"issue":"12","key":"pone.0305917.ref026","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3390\/min7120243","article-title":"Joint Application of Fractal Analysis and Weights-of-Evidence Method for Revealing the Geological Controls on Regional-Scale Tungsten Mineralization in Southern Jiangxi Province, China[J].","volume":"7","author":"T Sun","year":"2017","journal-title":"Minerals"},{"issue":"12","key":"pone.0305917.ref027","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1016\/j.cageo.2010.09.014","article-title":"Support vector machine: A tool for mapping mineral prospectivity[J].","volume":"37","author":"R Zuo","year":"2011","journal-title":"Computers & Geosciences"},{"issue":"11","key":"pone.0305917.ref028","doi-asserted-by":"crossref","first-page":"766","DOI":"10.3390\/ijgi10110766","article-title":"Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm\u2013Support Vector Machine (GA\u2013SVM) Model[J].","volume":"10","author":"X Du","year":"2021","journal-title":"ISPRS International Journal of Geo-Information"},{"issue":"1","key":"pone.0305917.ref029","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s11053-019-09510-8","article-title":"Mapping Mineral Prospectivity via Semi-supervised Random Forest[J].","volume":"29","author":"J Wang","year":"2020","journal-title":"Natural Resources Research"},{"issue":"1","key":"pone.0305917.ref030","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s11053-019-09564-8","article-title":"Prospectivity Mapping for Tungsten Polymetallic Mineral Resources, Nanling Metallogenic Belt, South China: Use of Random Forest Algorithm from a Perspective of Data Imbalance[J]","volume":"29","author":"T Li","year":"2020","journal-title":"Natural Resources Research"},{"key":"pone.0305917.ref031","doi-asserted-by":"crossref","first-page":"104316","DOI":"10.1016\/j.oregeorev.2021.104316","article-title":"Mineral prospectivity mapping by deep learning method in Yawan-Daqiao area, Gansu[J]","volume":"138","author":"Y Xu Y","year":"2021","journal-title":"Ore Geology Reviews"},{"key":"pone.0305917.ref032","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s11053-020-09742-z","article-title":"Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping[J].","volume":"30","author":"T Li","year":"2021","journal-title":"Natural Resources Research"},{"issue":"12","key":"pone.0305917.ref033","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s12145-018-00377-6","article-title":"Performance evaluation of RBF- and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of S-A multifractal model and mineralization controls[J]","volume":"3","author":"R Ghezelbash","year":"2019","journal-title":"Earth Science Informatics"},{"key":"pone.0305917.ref034","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s11053-017-9345-4","article-title":"Machine Learning of Mineralization-Related Geochemical Anomalies: A Review of Potential Methods[J].","volume":"26","author":"R. Zuo","year":"2017","journal-title":"Natural Resources Research"},{"key":"pone.0305917.ref035","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1023\/A:1025171803637","article-title":"Artificial Neural Networks for Mineral\u2014Potential Mapping: A Case Study from Aravalli Province, Western India[J].","volume":"12","author":"A Porwal","year":"2003","journal-title":"Natural Resource Research"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0305917","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T19:53:15Z","timestamp":1722455595000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0305917"}},"subtitle":[],"editor":[{"given":"Joanna","family":"Tindall","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,7,31]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7,31]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0305917","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,31]]}}}