View in EDS

Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.

Saved in:
Bibliographic Details
Title: Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.
Authors: Kendall GM; Cancer Epidemiology Unit, NDPH, University of Oxford, Richard Doll Building, Old Road Campus, Headington, Oxford, OX3 7LF, UK., Appleton JD; British Geological Survey, Kingsley Dunham Centre, Nicker Hill, Keyworth, Nottingham, NG12 5GG, UK., Chernyavskiy P; Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908-0717, USA., Arsham A; Center for Data, Mathematical & Computational Sciences, Goucher College, Baltimore, MD, USA., Little MP; Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, DHHS, NIH, Bethesda, MD, 20892-9778, USA; Faculty of Health and Life Sciences, Oxford Brookes University, Headington Campus, Oxford, OX3 0BP, UK. Electronic address: mark.little@nih.gov.
Source: Journal of environmental radioactivity [J Environ Radioact] 2025 Feb; Vol. 282, pp. 107595. Date of Electronic Publication: 2024 Dec 27.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 8508119 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1700 (Electronic) Linking ISSN: 0265931X NLM ISO Abbreviation: J Environ Radioact Subsets: MEDLINE
Database: MEDLINE Ultimate
FullText Text:
  Availability: 0
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 39731985
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Kendall+GM%22">Kendall GM</searchLink>; Cancer Epidemiology Unit, NDPH, University of Oxford, Richard Doll Building, Old Road Campus, Headington, Oxford, OX3 7LF, UK.<br /><searchLink fieldCode="AU" term="%22Appleton+JD%22">Appleton JD</searchLink>; British Geological Survey, Kingsley Dunham Centre, Nicker Hill, Keyworth, Nottingham, NG12 5GG, UK.<br /><searchLink fieldCode="AU" term="%22Chernyavskiy+P%22">Chernyavskiy P</searchLink>; Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908-0717, USA.<br /><searchLink fieldCode="AU" term="%22Arsham+A%22">Arsham A</searchLink>; Center for Data, Mathematical & Computational Sciences, Goucher College, Baltimore, MD, USA.<br /><searchLink fieldCode="AU" term="%22Little+MP%22">Little MP</searchLink>; Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, DHHS, NIH, Bethesda, MD, 20892-9778, USA; Faculty of Health and Life Sciences, Oxford Brookes University, Headington Campus, Oxford, OX3 0BP, UK. Electronic address: mark.little@nih.gov.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%228508119%22">Journal of environmental radioactivity</searchLink> [J Environ Radioact] 2025 Feb; Vol. 282, pp. 107595. <i>Date of Electronic Publication: </i>2024 Dec 27.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier+Applied+Science+Publishers%22">Elsevier Applied Science Publishers </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>8508119 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1879-1700 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%220265931X%22">0265931X </searchLink><i>NLM ISO Abbreviation: </i>J Environ Radioact <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=39731985
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.jenvrad.2024.107595
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 107595
    Titles:
      – TitleFull: Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kendall GM
      – PersonEntity:
          Name:
            NameFull: Appleton JD
      – PersonEntity:
          Name:
            NameFull: Chernyavskiy P
      – PersonEntity:
          Name:
            NameFull: Arsham A
      – PersonEntity:
          Name:
            NameFull: Little MP
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: 2025 Feb
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-electronic
              Value: 1879-1700
          Numbering:
            – Type: volume
              Value: 282
          Titles:
            – TitleFull: Journal of environmental radioactivity
              Type: main
ResultId 1