Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.
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| Title: | Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology. |
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| 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 |
| ISSN: | 1879-1700 |
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| DOI: | 10.1016/j.jenvrad.2024.107595 |
