Zobrazeno 1 - 10
of 14 103
pro vyhledávání: '"Senni A"'
Autor:
Croft, W. N., Lang, W. H.
Publikováno v:
Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 1942 Jun . 231(579), 131-163.
Externí odkaz:
https://www.jstor.org/stable/92331
Autor:
Leippold, Markus, Vaghefi, Saeid Ashraf, Stammbach, Dominik, Muccione, Veruska, Bingler, Julia, Ni, Jingwei, Colesanti-Senni, Chiara, Wekhof, Tobias, Schimanski, Tobias, Gostlow, Glen, Yu, Tingyu, Luterbacher, Juerg, Huggel, Christian
This paper presents Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims. Utilizing an array of Large Language Models (LLMs) informed by authoritative sources like the IPCC reports and peer-reviewed scient
Externí odkaz:
http://arxiv.org/abs/2401.12566
Autor:
Schimanski, Tobias, Senni, Chiara Colesanti, Gostlow, Glen, Ni, Jingwei, Yu, Tingyu, Leippold, Markus
Nature is an amorphous concept. Yet, it is essential for the planet's well-being to understand how the economy interacts with it. To address the growing demand for information on corporate nature disclosure, we provide datasets and classifiers to det
Externí odkaz:
http://arxiv.org/abs/2312.17337
Autor:
Luca Fazzini, Arianna Ghirardi, Raul Limonta, Alice Calabrese, Emilia D'Elia, Paolo Canova, Alessandra Fontana, Aurelia Grosu, Attilio Iacovoni, Paola Ferrari, Renata De Maria, Antonello Gavazzi, Roberta Montisci, Michele Senni, Mauro Gori
Publikováno v:
ESC Heart Failure, Vol 11, Iss 5, Pp 3350-3359 (2024)
Abstract Aims The identification of subjects at higher risk for incident heart failure (HF) with preserved ejection fraction (EF) suitable for more intensive preventive programmes remains challenging. We applied phenomapping to the DAVID‐Berg popul
Externí odkaz:
https://doaj.org/article/d0f75b28c55f4a42bf7c0ee6e3847ae0
Autor:
Senni, Chiara Colesanti1 (AUTHOR) chiara.colesantisenni@df.uzh.ch, Goel, Skand2 (AUTHOR) skand.goel@spglobal.com
Publikováno v:
Journal of Impact & ESG Investing. Winter2024, Vol. 5 Issue 2, p60-92. 33p.
Autor:
Ni, Jingwei, Bingler, Julia, Colesanti-Senni, Chiara, Kraus, Mathias, Gostlow, Glen, Schimanski, Tobias, Stammbach, Dominik, Vaghefi, Saeid Ashraf, Wang, Qian, Webersinke, Nicolas, Wekhof, Tobias, Yu, Tingyu, Leippold, Markus
In the face of climate change, are companies really taking substantial steps toward more sustainable operations? A comprehensive answer lies in the dense, information-rich landscape of corporate sustainability reports. However, the sheer volume and c
Externí odkaz:
http://arxiv.org/abs/2307.15770
Autor:
Ni, Jingwei, Bingler, Julia, Colesanti-Senni, Chiara, Kraus, Mathias, Gostlow, Glen, Schimanski, Tobias, Stammbach, Dominik, Vaghefi, Saeid Ashraf, Wang, Qian, Webersinke, Nicolas, Wekhof, Tobias, Yu, Tingyu, Leippold, Markus
This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (T
Externí odkaz:
http://arxiv.org/abs/2306.15518
Autor:
Vaghefi, Saeid Ashraf, Wang, Qian, Muccione, Veruska, Ni, Jingwei, Kraus, Mathias, Bingler, Julia, Schimanski, Tobias, Colesanti-Senni, Chiara, Webersinke, Nicolas, Huggel, Christrian, Leippold, Markus
Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges: hallucination and outdated information after the training phase
Externí odkaz:
http://arxiv.org/abs/2304.05510