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Sökning: WFRF:(Mita T) > (2020-2023)

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1.
  • Zeichner, Sarah S., et al. (författare)
  • Polycyclic aromatic hydrocarbons in samples of Ryugu formed in the interstellar medium
  • 2023
  • Ingår i: Science. - : AMER ASSOC ADVANCEMENT SCIENCE. - 0036-8075 .- 1095-9203. ; 382:6677, s. 1411-1415
  • Tidskriftsartikel (refereegranskat)abstract
    • Polycyclic aromatic hydrocarbons (PAHs) contain less than or similar to 20% of the carbon in the interstellar medium. They are potentially produced in circumstellar environments (at temperatures greater than or similar to 1000 kelvin), by (similar to 10 kelvin) interstellar clouds, or by processing of carbon-rich dust grains. We report isotopic properties of PAHs extracted from samples of the asteroid Ryugu and the meteorite Murchison. The doubly-C-13 substituted compositions (Delta 2x(13)C values) of the PAHs naphthalene, fluoranthene, and pyrene are 9 to 51 parts per thousand higher than values expected for a stochastic distribution of isotopes. The Delta 2x(13)C values are higher than expected if the PAHs formed in a circumstellar environment, but consistent with formation in the interstellar medium. By contrast, the PAHs phenanthrene and anthracene in Ryugu samples have Delta 2x(13)C values consistent with formation by higher-temperature reactions.
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2.
  • Kino, S., et al. (författare)
  • A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects
  • 2021
  • Ingår i: SSM - Population Health. - : Elsevier BV. - 2352-8273. ; 15
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). Methods: Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. Results: Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). Conclusions: While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness. © 2021
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