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Jiaao Chen

Datos Biográficos

ID4004659
NOMBREJiaao Chen
NOMBRESJiaao
APELLIDOChen
FIRMACHEN J
AFILIACIONESCentral South University of Forestry and Technology
VERIFICADONo
TOTAL DE OBRAS5
TOTAL DE CITAS49
TOTAL COMO AUTOR5
TOTAL COMO EDITOR0
PRIMER AÑO DE PUBLICACIÓN2023
AÑO MÁS RECIENTE DE PUBLICACIÓN2026
ÍNDICE H1
  • Attractiveness of urban open spaces in the information Era

    Open Access•Yun Li, Yurou Li et al.•ARTICLE•Landscape and Urban Planning•2026

  • Insights into the risk of Covid-19 infection in urban neighborhood and its environmental influence factors

    Open Access•Peng Xiao, Dongrui Zhao et al.•ARTICLE•Sustainable Cities and Society•2024

  • Mapping urban green equity and analysing its impacted mechanisms

    Open Access•Yuchi Cao, Yan Li et al.•ARTICLE•Sustainable Cities and Society•2024

  • Quantifying and mapping landscape value using online texts

    Open Access•Jingpeng Liao, Qiulin Liao et al.•ARTICLE•Applied Geography•2023

  • Can Large Language Models Transform Computational Social Science

    Open Access•Caleb Ziems, William A Held et al.•ARTICLE•Computational Linguistics•2023•Citada por: 49•Referencias: 27

    Large language models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the computational social science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompti…

  • Can Large Language Models Transform Computational Social Science

    Open Access•Caleb Ziems, William A Held et al.•ARTICLE•Computational Linguistics•2023•Citada por: 49•Referencias: 27

    Large language models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the computational social science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompti…

  • Quantifying and mapping landscape value using online texts

    Open Access•Jingpeng Liao, Qiulin Liao et al.•ARTICLE•Applied Geography•2023

  • Can Large Language Models Transform Computational Social Science

    Open Access•Caleb Ziems, William A Held et al.•ARTICLE•Computational Linguistics•2023•Citada por: 49•Referencias: 27

    Large language models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the computational social science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompti…

  • Insights into the risk of Covid-19 infection in urban neighborhood and its environmental influence factors

    Open Access•Peng Xiao, Dongrui Zhao et al.•ARTICLE•Sustainable Cities and Society•2024

  • Mapping urban green equity and analysing its impacted mechanisms

    Open Access•Yuchi Cao, Yan Li et al.•ARTICLE•Sustainable Cities and Society•2024

  • Attractiveness of urban open spaces in the information Era

    Open Access•Yun Li, Yurou Li et al.•ARTICLE•Landscape and Urban Planning•2026

Geography (3 obras) · Land Use and Ecosystem Services (3 obras) · Environmental planning (2 obras) · Perception (2 obras) · Urban Green Space and Health (2 obras) · Urban planning (2 obras) · 2019-20 coronavirus outbreak (1 obras) · Artificial Intelligence (1 obras) · Artificial Intelligence (1 obras) · Attractiveness (1 obras)

Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae