[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123827-en":3,"doc-seo-123827-105":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},123827,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Ocean-related options for climate change mitigation and adaptation - A machine learning-based evidence map protocol","Ocean-related options (OROs) to mitigate and adapt to climate change are gaining attention from practitioners, decision-makers, and researchers. Guiding future ORO development and implementation requires a rigorous catalogue of scientific evidence linked to outcomes across different ORO types. Existing synthesis efforts have been hindered by challenges in aggregating and evaluating evidence at scale, motivating the protocol for constructing a machine learning-based evidence map.","PR IFYS GOL BANGOR / BANGOR  \nOcean-related options for climate change mitigation and adaptation: A machine learning-based evidence map protocol  \nVeytia, Devi; Airoldi, Laura; Paris, PSL; Cooley, Sarah; Magnan, Alexandre; Marti, Vicky; Neill, Simon; Sumaila, Rashid; Thebaud, Olivier; Voolstra, Christian; Williamson, Phillip; Bonin, Marie; Langridge, Joseph; Comte, Adrien; Viard, Frederique; Shin, Yunne; Bopp, Laurent; Gattuso, Jean-Pierre  \nProtocol Exchange  \nDOI:  \n10.21203/rs.3.pex-2324/v1  \nPublished: 01/02/2024  \nPeer reviewed version  \nCyswllt i'r cyhoeddiad / Link to publication  \nDyfyniad o'r fersiwn a gyhoeddwyd / Citation for published version (APA):  \nVeytia, D. , Airoldi, L. , Paris, PSL. , Cooley, S. , Magnan, A. , Marti, V. , Neill, S. , Sumaila, R. , Thebaud, O. , Voolstra, C. , Williamson, P. , Bonin, M. , Langridge, J. , Comte, A. , Viard, F. , Shin, Y. , Bopp, L. , & Gattuso, J.-P. (2024) . Ocean-related options for climate change mitigation and adaptation: A machine learning-based evidence map protocol. Protocol Exchange.  \n[https://doi.org/10.21203/rs.3.pex-2324/v1](https://doi.org/10.21203/rs.3.pex-2324/v1)  \nHawliau Cyffredinol / General rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal ?  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \n􀂦   \nFRB-CESAB, Fondation pour la Recherche sur la Biodiversité – Centre de Synthèse et d'Analyse sur la Biodiversité, 5 rue de l'école de médecine 34000 Montpellier, France [https://orcid.org/0000-0001-6268-](https://orcid.org/0000-0001-6268-)[ ](https://orcid.org/0000-0001-6268-)1508  \nChioggia Hydrobiological Station “Umberto D’Ancona”, Department of Biology, University of Padova, Uo CoNISMa, Chioggia, 30015, Italy; NBFC, National Biodiversity Future Center, Palermo 90133, Italy  \n[https://orcid.org/0000-0001-5046-0871](https://orcid.org/0000-0001-5046-0871)  \nNational Center for Scienti¦c Research; PSL Université Paris, CRIOBE, CNRS-EPHE-UPVD, Maison del’Océan, 195 rue Saint-Jacques 75013 Paris, France [https://orcid.org/0000-0001-6295-1061](https://orcid.org/0000-0001-6295-1061)  \nOcean Conservancy, 1300 19th St. NW, Suite 800, Washington DC 20036 USA [https://orcid.org/0000-](https://orcid.org/0000-)[ ](https://orcid.org/0000-)0002-1034-0653  \nInstitute for Sustainable Development and International Relations, Sciences Po, Paris, France [https://orcid.org/0000-0001-7421-5184](https://orcid.org/0000-0001-7421-5184)  \nCivil and Environmental Engineering, University of Strathclyde, Glasgow, UK [https://orcid.org/0000-](https://orcid.org/0000-)[ ](https://orcid.org/0000-)0001-9547-3377  \nSchool of Ocean Sciences, Bangor University, Menai Bridge, UK [https://orcid.org/0000-0002-1674-3445](https://orcid.org/0000-0002-1674-3445)  \nInstitute for the Oceans and Fisheries and School of Public Policy and Global Affairs, University of British Columbia, 2202 Main Mall, Vancouver, B.C., V6T 1Z4, Canada [https://orcid.org/0000-0002-1851-](https://orcid.org/0000-0002-1851-)[ ](https://orcid.org/0000-0002-1851-)1621  \nIfremer, AMURE, Plouzané, 29280, France [https://orcid.org/0000-0001-8665-3827](https://orcid.org/0000-0001-8665-3827)  \nDepartment of Biology, University of Konstanz, Konstanz, Germany [https://orcid.org/0000-0003-4555-](https://orcid.org/0000-0003-4555-)[ ](https://orcid.org/00","cbCainynKl3qkAAS","https://ap.wps.com/l/cbCainynKl3qkAAS","pdf",1867230,1,27,"English","en",105,"# Ocean-related options and need for synthesis\n## Evidence catalogue for ORO outcomes\n## Machine learning-based evidence map protocol","[{\"question\":\"What is the document’s focus on ocean-related options (OROs)?\",\"answer\":\"It addresses how OROs can mitigate and adapt to climate change, emphasizing the need for evidence supporting different ORO types and their outcomes.\"},{\"question\":\"Why is an evidence catalogue important for ORO development and implementation?\",\"answer\":\"A catalogue of scientific evidence is critical to guide future ORO development and to support decision-making based on outcome-related findings.\"},{\"question\":\"What role does machine learning play in the proposed approach?\",\"answer\":\"The protocol uses a machine learning-based evidence map to overcome difficulties in synthesizing and evaluating evidence at scale across ORO categories.\"}]","Ocean-related options for climate change mitigation and adaptation - 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