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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Mandrioli Molecules2019 24-11.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:Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L.|Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L.]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


[[wikipedia:Cannabis|Cannabis]] has regained much attention as a result of updated legislation authorizing many different uses, and it can be classified on the basis of the content of [[wikipedia:Tetrahydrocannabinol|Δ9-tetrahydrocannabinol]] (Δ9-THC), a psychotropic substance for which there are legal limitations in many countries. For this purpose, accurate qualitative and quantitative determination is essential. The relationship between THC and [[wikipedia:Cannabidiol|cannabidiol]] (CBD) is also significant, as the latter substance is endowed with many specific and non-psychoactive proprieties. For these reasons, it becomes increasingly important and urgent to utilize fast, easy, validated, and harmonized procedures for determination of [[wikipedia:Cannabinoid|cannabinoids]]. The procedure described herein allows rapid determination of 10 cannabinoids from the [[wikipedia:Inflorescence|inflorescences]] of ''Cannabis sativa'' L. by extraction with organic solvents. Separation and subsequent detection are by [[wikipedia:Reversed-phase chromatography|reversed-phase]] [[high-performance liquid chromatography]] with ultraviolet detector (RP-HPLC-UV). ('''[[Journal:Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L.|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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