35 research outputs found

    MeMoVolc report on classification and dynamics of volcanic explosive eruptions

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    Classifications of volcanic eruptions were first introduced in the early twentieth century mostly based on qualitative observations of eruptive activity, and over time, they have gradually been developed to incorporate more quantitative descriptions of the eruptive products from both deposits and observations of active volcanoes. Progress in physical volcanology, and increased capability in monitoring, measuring and modelling of explosive eruptions, has highlighted shortcomings in the way we classify eruptions and triggered a debate around the need for eruption classification and the advantages and disadvantages of existing classification schemes. Here, we (i) review and assess existing classification schemes, focussing on subaerial eruptions; (ii) summarize the fundamental processes that drive and parameters that characterize explosive volcanism; (iii) identify and prioritize the main research that will improve the understanding, characterization and classification of volcanic eruptions and (iv) provide a roadmap for producing a rational and comprehensive classification scheme. In particular, classification schemes need to be objective-driven and simple enough to permit scientific exchange and promote transfer of knowledge beyond the scientific community. Schemes should be comprehensive and encompass a variety of products, eruptive styles and processes, including for example, lava flows, pyroclastic density currents, gas emissions and cinder cone or caldera formation. Open questions, processes and parameters that need to be addressed and better characterized in order to develop more comprehensive classification schemes and to advance our understanding of volcanic eruptions include conduit processes and dynamics, abrupt transitions in eruption regime, unsteadiness, eruption energy and energy balance

    Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

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    A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings

    Soliciting hydrothermal systems: the case of La Soufrière of Guadeloupe (FWI) unrest

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    International audienceWe have re-assessed the main physicochemical features of the hydrothermal system of La Soufrière of Guadeloupe (FWI) andesitic volcano. A careful analysis of different techniques adopted historically for gas sampling and analysis by OVSG has allowed us to extend the use of our modeling and of gas thermobarometers as back as possible to the last 20 years, also including data from the 1976-77 phreatic eruption. Our aim was to track the evolution of P-T conditions of the gas equilibrium zone within the hydrothermal system. Our results show that long-term fluctuations characterize the behavior of the hydrothermal system in relation to injections of more magmatic deeper-sourced fluid into the hydrothermal system. This oscillations may lead to P-T excursions around and over the critical point of water, which destabilize the hydrothermal system. Whether such long-term increases reflect an oscillating behavior of the deep source injecting fluids upward, or are due to the modulation determined by the volcano structure via the many structures dissecting the edifice and relaxing the accumulated tensions, is matter of the ongoing investigations. Nevertheless, available data show that the recent unrest phase recorded between February and the end of April 2018 (Moretti et al., submitted; see also OVSG-IPGP reports) was due to the temperature increase and pressure build-up of the hydrothermal system, which originated a rapidly occurring (in the order of days) but sharp peak in monitored geochemical quantities. Therefore, scenarios that could lead to the sudden decompression of critical fluids must be considered in monitoring strategies and risk analysis
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