[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119772-en":3,"doc-seo-119772-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119772,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","The First Generation of Machine Learning Applications for Tracking Climate Change Adaptation - Doctor of Philosophy Thesis","This doctoral thesis addresses practical difficulties in designing a meaningful climate change adaptation tracking system. It proposes a solution using modern machine learning to analyze large volumes of adaptation-relevant information and develops three concrete, dataset-specific applications of machine learning methods. A fourth publication contextualizes and synthesizes the findings, highlighting lessons learned and pathways to improve future adaptation tracking approaches.","The First Generation of Machine Learning Applications for Tracking Climate Change Adaptation  \nAnne Jelmar Sietsma  \nSubmitted in accordance with the requirements for the degree of  \nDoctor of Philosophy  \nThe University of Leeds Priestley International Centre for Climate School of Geography  \nJuly 2023  \nThe candidate confirms that the work submitted is their own, except where work which has formed part of jointly authored publications has been included. The contribution of the candidate and the other authors to this work has been explicitly indicated below. The candidate confirms that appropriate credit has been given within the thesis where reference has been made to the work of others.  \nThe work in chapter 2 of this thesis has appeared in publication as follows, with additions for the format of the thesis:  \nSietsma, A.J., Ford, J.D., Callaghan, M.W. and Minx, J.C. (2021) . Progress in climate change adaptation research. Environmental Research Letters, 16(5), p.054038 . doi: 10.1088/1748- 9326/abf7f3 .  \nContributions for this work: Conceptualisation, AJS, JDF andJCM; Methodology, AJS, JCM, JDF; Software, AJS and MC; Investigation, AJS; Data Curation, AJS and MWC; Writing – Original Draft, AJS; Writing – Reviewing & Editing, AJS, JCM andJDF; Visualization, AJS; Supervision, JCM andJDF.  \nThe work in chapter 3 of this thesis has been submitted for publication and has received permission for re-submission as follows, with additions for the format of the thesis:  \nSietsma, A.J., Theokritoff, E., Biesbroek, R., Villaverde Canosa, I., Thomas, A., Callaghan, M., Minx, J. and Ford, J.D. (2023) . [under review]. Climate Change Adaptation Policy Across Scales: A Machine Learning Evidence Map. Submitted to One Earth. First version available at  \nSSRN 4318820 . doi: 10.2139/ssrn.4318820.  \nContributions for this work: Conceptualisation, AJS, RB, MC, JCM andJDF; Methodology, AJS, ET, RB, AT, MC, JCM andJDF; Software, AJS and MC; Investigation, AJS, ET, AT and IVC; Data Curation, AJS; Writing – Original Draft, AJS; Writing – Reviewing & Editing, AJS, ET, RB, AT, JCM andJDF; Visualization, AJS and MC; Supervision, JCM andJDF.  \nThe work in chapter 4 of this thesis has been submitted for publication and has received permission for re-submission as follows, with additions for the format of the thesis:  \nWright, S., Sietsma, A.J., Korswagen, S., Athanasiadis, I. N., and Biesbroek, R. (2023) . [under review] . How do countries frame climate change? A global comparison of adaptation and mitigation in UNFCCC National Communications. Submitted to Global Environmental Change.  \nContributions for this work: Conceptualisation, SW, AJS, SK, INA and RB; Methodology, SW, AJS and RB; Software, SW and AJS; Investigation, SW, AJS, SK and RB; Data Curation, SW, AJS and SK; Formal analysis, SW and AJS; Validation, AJS; Writing – Original Draft, SW, AJS and SK; Writing – Reviewing & Editing, SW, AJS, SK, INA; Visualization, SW and AJS; Supervision, RB and INA.  \nThe work in chapter 5 of this thesis has been submitted for publications and has received permission for re-submission as follows, with additions for the format of the thesis:  \nSietsma, A.J., Ford, J. D. and Minx, J. C. (2023) . [under review] . Machine Learning for Adaptation Tracking: Shaping the Next Generation of Text Analysis. Submitted to Nature Climate Change.  \nContributions for this work: Conceptualisation, AJS, JDF andJCM; Investigation, AJS; Data Curation, AJS; Writing – Original Draft, AJS; Writing – Reviewing & Editing, AJS, JDF and JCM; Supervision, JDF andJCM.  \nThis copy has been supplied on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement.  \nThe right Anne J. Sietsma to be identified as author of this work has been asserted by Anne J. Sietsma in accordance with the Copyright, Designs and Patents Act 1988.  \nThis thesis begins by outlining a broad problem: the practical difficulties of designing ","cbCaipYe1mqtGI53","https://ap.wps.com/l/cbCaipYe1mqtGI53","pdf",6344065,1,232,"English","en",105,"# Chapter Overview\n## Problem and Proposed Solution\n## Three Machine Learning Applications\n## Fourth Publication and Integration\n## Contributions and Acknowledgements","[{\"question\":\"What problem does the thesis focus on?\",\"answer\":\"The thesis focuses on the practical challenges of designing a meaningful climate change adaptation tracking system.\"},{\"question\":\"What solution does the thesis propose?\",\"answer\":\"It proposes using modern machine learning methods to analyze large volumes of adaptation-relevant information.\"},{\"question\":\"How is the thesis organized in terms of research outputs?\",\"answer\":\"It develops three distinct applications as separate publication efforts and includes a fourth publication that places previous findings into a broader perspective and discusses improvements for future tracking.\"}]","The First Generation of Machine Learning Applications for Tracking Climate Change Adaptation - 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