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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Oyashi GeospatialHlth2019 14-1.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png|240px]]</div>
'''"[[Journal:Japan Aerospace Exploration Agency’s public-health monitoring and analysis platform: A satellite-derived environmental information system supporting epidemiological study|Japan Aerospace Exploration Agency’s public-health monitoring and analysis platform: A satellite-derived environmental information system supporting epidemiological study]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


Since the 1970s, Earth-observing satellites collect increasingly detailed [[Environmental monitoring|environmental information]] on land cover, meteorological conditions, environmental variables, and air pollutants. This [[information]] spans the entire globe, and its acquisition plays an important role in epidemiological analysis when ''in situ'' data are unavailable or spatially and/or temporally sparse. In this paper, we present the development of the Japan Aerospace Exploration Agency’s (JAXA) Public-health Monitoring and Analysis Platform, a user-friendly, web-based system providing environmental data on shortwave radiation, rainfall, soil moisture, the normalized difference vegetation index, aerosol optical thickness, land surface temperature and altitude. ('''[[Journal:Japan Aerospace Exploration Agency’s public-health monitoring and analysis platform: A satellite-derived environmental information system supporting epidemiological study|Full article...]]''')<br />
The introduction of [[ChatGPT]] has fuelled a public debate on the appropriateness of using generative [[artificial intelligence]] (AI) ([[large language model]]s or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (''N'' = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... ('''[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Full article...]]''')<br />
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Revision as of 15:26, 20 May 2024

Fig1 Niszczota EconBusRev23 9-2.png

"Judgements of research co-created by generative AI: Experimental evidence"

The introduction of ChatGPT has fuelled a public debate on the appropriateness of using generative artificial intelligence (AI) (large language models or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (N = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... (Full article...)
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