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  1. Mar 2024
  2. Local file Local file
    Machine Learning for Improving Surface-Layer-Flux Estimates
    1
    1. caozhuyin 10 Mar 2024
      Abstract

      结论：预测结果，好于MOST（MO估计系统地低估了湍流通量的大小，改善了与观测值和减小与观测通量偏离的总幅度。）,不同地点的泛化能力 不足：不含物质通量，预测结果待提升，结果因稳定性而异常，不同季节的泛化能力，运用了不易获得的变量（找到最小观测集）

      This effort demonstrates the potential of ML models to overcome some of the limitations related to the simple regression fits employed to determine the stability correction parameter (only a function of the inverse Obukhov length) and other limitations inherent to the MO formulation of surface-layer fluxes (e.g., assumed homogeneous and stationary conditions).

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  3. Feb 2021
  4. www.pnas.org www.pnas.org
    The diversity bonus in pooling local knowledge about complex problems
    1
    1. jasminehollingworth 15 Feb 2021

      Aminpour, P., Gray, S. A., Singer, A., Scyphers, S. B., Jetter, A. J., Jordan, R., Murphy, R., & Grabowski, J. H. (2021). The diversity bonus in pooling local knowledge about complex problems. Proceedings of the National Academy of Sciences, 118(5). https://doi.org/10.1073/pnas.2016887118

      lang:en is:article group diversity diverse problem complex solve social environmental knowledge ecosystem homogeneous model
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    pnas.org/content/118/5/e2016887118
  5. Aug 2020
  6. www.nber.org www.nber.org
    Social Interactions in Pandemics: Fear, Altruism, and Reciprocity
    1
    1. Marlene_Wulf 11 Aug 2020

      Alfaro, L., Faia, E., Lamersdorf, N., & Saidi, F. (2020). Social Interactions in Pandemics: Fear, Altruism, and Reciprocity (Working Paper No. 27134; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27134

      is:preprint lang:en social interaction pandemic fear altruism reciprocity SIR model homogeneous network structure infection rate exogenous behavior social interactionlockdown policy
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    nber.org/papers/w27134