Over 2023, many universities and policy organisations in the higher education (HE) sector are working to create guiding principles and guidelines for the use of generative artificial intelligence (AI) in HE Teaching and Learning (T&L). Despite these guidelines, students remain unsure if and how they should use AI. This article discusses the AI information sessions held over the Autumn 2023 term in the Department of Classics at the University of Reading, which aimed to provide students with the knowledge and tools to make informed judgements about using AI in their studies. These sessions discussed the benefits and drawbacks of generative AI, highlighting training data, content policy, environmental impact, and examples of potential uses. Staff and student participants were surveyed before and after these information sessions to gather their opinions surrounding AI use. Although at least 60% of participants had previously used generative AI, 80% of participants were apprehensive of or against using generative AI tools for learning purposes following the AI information sessions. By providing staff and students with the ethical considerations surrounding generative AI, they can make an informed judgement about using AI in their work without misplaced faith or excessive fear.

In November 2022, ChatGPT 3.5 was released on a public research preview, gaining notoriety for its ability to pull from a vast body of information to create coherent and digestible bodies of text that accurately respond to queries (OpenAI, 2022). It is able to recognise the grammar and vocabulary of ancient languages, translate passages, and compose texts at an alarmingly accurate and rapid rate. For teachers, this AI has had mixed reviews. Some fear its ability to produce well-written work effortlessly, while others are excited by its abilities to push the boundaries of current teaching practices. This paper explores how well ChatGPT explains grammatical concepts, parses inflected forms, and translates Classical Latin, Ancient Greek, and Classical Sanskrit. Overall, ChatGPT is rather good at working with Classical Latin and Sanskrit, but its abilities with Ancient Greek are deeply problematic. Although it is quite flawed at this time, ChatGPT, when used properly, could become a useful a tool for ancient language study. With proper guiding phrases, students could use this AI to practise vocabulary, check their translations, and rephrase grammatical concepts.

Quaternary processes and environmental changes are often difficult to assess in remote subantarctic islands due to high surface erosion rates and overprinting of sedimentary products in locations that can be a challenge to access. We present a set of high-resolution, multichannel seismic lines and complementary multibeam bathymetry collected off the eastern (leeward) side of the subantarctic Auckland Islands, about 465 km south of New Zealand's South Island. These data constrain the erosive and depositional history of the island group, and they reveal an extensive system of sediment-filled valleys that extend offshore to depths that exceed glacial low-stand sea level. Although shallow, marine, U-shaped valleys and moraines are imaged, the rugged offshore geomorphology of the paleovalley floors and the stratigraphy of infill sediments suggests that the valley floors were shaped by submarine fluvial erosion, and subsequently filled by lacustrine, fjord, and fluvial sedimentary processes.

Tree-ring reconstructions of temperature often target trees at altitudinal or latitudinal tree line where annual growth is broadly expected to be limited by and respond to temperature variability. Based on this principal, regions with sparse tree line would seem to be restricted in their potential to reconstruct past temperatures. In the northeastern United States, there are only two published temperature reconstructions. Previous work in the region reconstructing moisture availability, however, has shown that using a greater diversity of species can improve reconstruction model skill. Here, we use a network of 228 tree-ring records composed of 29 species to test the hypothesis that an increase in species diversity among the pool of predictors improves reconstructions of past temperatures. Chamaecyparis thyoides alone explained 31% of the variability in observed cool-season minimum temperatures, but a multispecies model increased the explained variance to 44%. Liriodendron tulipifera, a species not previously used for temperature reconstructions, explained a similar amount of variance as Chamaecyparis thyoides (12.9% and 20.8%, respectively). Increasing the species diversity of tree proxies has the potential for improving reconstruction of paleotemperatures in regions lacking latitudinal or elevational tree lines provided that long-lived hardwood records can be located.

Hospitalized older adults are at high risk of falling. The HELPER system is a ceiling-mounted fall detection system that sends an alert to a smartphone when a fall is detected. This article describes the performance of the HELPER system, which was pilot tested in a geriatric mental health hospital. The system’s accuracy in detecting falls was measured against the hospital records documenting falls. Following the pilot test, nurses were interviewed regarding their perceptions of this technology. In this study, the HELPER system missed one documented fall but detected four falls that were not documented. Although sensitivity (.80) of the system was high, numerous false alarms brought down positive predictive value (.01). Interviews with nurses provided valuable insights based on the operation of the technology in a real environment; these and other lessons learned will be particularly valuable to engineers developing this and other health and social care technologies.

To investigate the effects of both non-meltwater and meltwater-related post-depositional processes on chemical species within the snow–firn pack, a research program, the Program for Glacier Processes Investigation, was initiated in July 2002 by the Tien Shan Glaciological Station, Chinese Academy of Sciences. The seasonal variability of the ionic concentrations in surface snow samples and ion elution behavior in the snow–firn pack were assessed from surface samples collected year-round and 1011 samples collected from a snow pit at weekly intervals from September 2003 through September 2004. The results indicate that elevated ionic concentrations in spring and summer result from Asian dust-storm-derived aerosol input and other aerosols entrained in precipitation. Potential sources of these chemical species are explored using correlation and factor analyses. The elution sequence through the snow–firn pack was determined to be SO42– >Ca2+> Na+>NO3− >Cl−>K+> Mg2+ >NH4+. The elution of ions at the sampling site was found to be driven primarily by air temperature and became evident when a diurnal mean temperature of –3.6˚C was attained. At 0.3˚C all of the year-round new ionic input was leached from the snow.

In order to investigate the relationship between atmospheric nitrate and nitrate records in glacial snow and firn, nitrate was investigated in atmospheric aerosol, surface snow and the snow–firn pack on Ürümqi glacier No. 1 , eastern Tien Shan, China, from September 2002 to September 2004. The study found that vertical nitrate stratigraphy in the upper snowpack is formed during the winter by dry deposition and compaction and sublimation of the snowpack. During the summer the snowpack nitrate is redistributed through the percolation of meltwater. Nitrate was found to be highly mobile in the snowpack relative to magnesium.