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Search: WFRF:(Shuman Eric)

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1.
  • Grossmann, Igor, et al. (author)
  • Insights into the accuracy of social scientists' forecasts of societal change
  • 2023
  • In: Nature Human Behaviour. - : Springer Nature. - 2397-3374. ; 7, s. 484-501
  • Journal article (peer-reviewed)abstract
    • How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models.
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2.
  • Von Eggers, Jordan M., et al. (author)
  • Environmental filtering governs consistent vertical zonation in sedimentary microbial communities across disconnected mountain lakes
  • 2024
  • In: Environmental Microbiology. - : John Wiley & Sons. - 1462-2912 .- 1462-2920. ; 26:3
  • Journal article (peer-reviewed)abstract
    • Subsurface microorganisms make up the majority of Earth's microbial biomass, but ecological processes governing surface communities may not explain community patterns at depth because of burial. Depth constrains dispersal and energy availability, and when combined with geographic isolation across landscapes, may influence community assembly. We sequenced the 16S rRNA gene of bacteria and archaea from 48 sediment cores across 36 lakes in four disconnected mountain ranges in Wyoming, USA and used null models to infer assembly processes across depth, spatial isolation, and varying environments. Although we expected strong dispersal limitations across these isolated settings, community composition was primarily shaped by environmental selection. Communities consistently shifted from domination by organisms that degrade organic matter at the surface to methanogenic, low-energy adapted taxa in deeper zones. Stochastic processes—like dispersal limitation—contributed to differences among lakes, but because these effects weakened with depth, selection processes ultimately governed subsurface microbial biogeography.
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