{"created":"2023-05-15T14:59:13.964362+00:00","id":80365,"links":{},"metadata":{"_buckets":{"deposit":"b5bcd831-ad68-4d81-8ac9-64b16d2a60e7"},"_deposit":{"created_by":1,"id":"80365","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"80365"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00080365","sets":["1"]},"author_link":["885248","885252","885250","885249","885251"],"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-08","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"138","bibliographic_titles":[{"bibliographic_title":"Radiation Measurements"}]}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Interest in radon (Rn) is not limited only to its impact on health and its dose to the public, but due to its properties, the techniques to analyse its behavior can be used in many fields such as radiotherapy, atmospheric physics, geophysics, geohazards, mineral exploration, and even planetary science.\n\nNowadays machine learning methods provide extremely important tools for intelligent environmental data analysis, processing and visualization.\nWe describe application of machine learning to environmental sciences with an emphasis on the radon exhalation rate in order to express responses from multivariable time-series data collected at a measuring site near the Sakurajima volcano (Kagoshima, Japan).","subitem_description_type":"Abstract"}]},"item_8_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1016/j.radmeas.2020.106402","subitem_relation_type_select":"DOI"}}]},"item_8_relation_17":{"attribute_name":"関連サイト","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://www.sciencedirect.com/science/article/pii/S1350448720301815?dgcid=author","subitem_relation_type_select":"URI"}}]},"item_8_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1350-4487","subitem_source_identifier_type":"ISSN"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hosoda, M."}],"nameIdentifiers":[{"nameIdentifier":"885248","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Tokonami, S."}],"nameIdentifiers":[{"nameIdentifier":"885249","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Suzuki, T."}],"nameIdentifiers":[{"nameIdentifier":"885250","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Janik, Miroslaw"}],"nameIdentifiers":[{"nameIdentifier":"885251","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Janik, Miroslaw","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"885252","nameIdentifierScheme":"WEKO"}]}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Machine learning as a tool for analysing the impact of environmental parameters on the radon exhalation rate from soil","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Machine learning as a tool for analysing the impact of environmental parameters on the radon exhalation rate from soil"}]},"item_type_id":"8","owner":"1","path":["1"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-08-25"},"publish_date":"2020-08-25","publish_status":"0","recid":"80365","relation_version_is_last":true,"title":["Machine learning as a tool for analysing the impact of environmental parameters on the radon exhalation rate from soil"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T21:36:01.697009+00:00"}