Lichenosticta antarctica, a lichenicolous fungus, is described as a new species, collected from the Sør Rondane Mountains, inland eastern Antarctica. It is characterized by small conidiomata, irregularly branched conidiophores, chained conidiogenous cells and lacriform conidia. In addition, six other noteworthy lichenicolous fungi—Bryostigma cf. molendoi, Carbonea aggregantula, Intralichen lichenicola, Lichenoconium usneae, Muellerella pygmaea and Tetramelas phaeophysciae—are reported from the same site, representing new distribution records for inland eastern Antarctica. A new key to the known species of Lichenosticta is presented.
Adélie penguins (Pygoscelis adeliae) are vital bioindicators of environmental change in Antarctica. This study evaluated the state-of-the-art You Only Look Once version 11 (YOLOv11) deep learning-based object detection model and very high-resolution (VHR) drone images to comprehensively census nesting Adélie penguins on Cape Hallett, Ross Sea, Antarctica. A total of 554 drone images, acquired from four independent flights in November 2017, were mosaicked at a spatial resolution of approximately 7.1 mm and then segmented into small-size image tiles for nesting penguin detection. An overlap was applied between adjacent small image tiles to prevent the loss of detected nesting penguins caused by segmentation cuts. The detection results from the overlapping areas in adjacent image tiles were consolidated using a predefined distance threshold of 0.1 m to eliminate duplicate detections. The deep learning-based object detection model achieved a detection precision of 95.3%, a recall of 97.6% at a confidence level of 0.5 and an estimated population of 47 340 nesting penguins. The derived population estimate closely agreed with results from an aerial survey conducted in the same year. Some nesting penguins were missed in dark land areas where colour contrast was low, and nesting-penguin-like rocks, shadows and wet areas caused false detections. Nevertheless, the approach demonstrated high accuracy, efficiency and scalability. This study highlights the potential of combining VHR drone images and deep learning-based object detection for low-disturbance penguin monitoring, providing data to support conservation strategies and to assess the impacts of climate change and human activities on Antarctic ecosystems.
This case study examines how formative assessment performance, student engagement traces, and instructor feedback language co-vary with performance on a final integrative assessment. The study was conducted in a 12-week, second-year Framework for Higher Education Qualifications (FHEQ) Level 6) undergraduate course on empirical analysis and the scientific method (N = 49). We triangulate low-stakes assessments, platform-generated engagement indicators (talktime, hand raises, votes, reactions, and chat messages), and Linguistic Inquiry and Word Count 2022 (LIWC-22) features of instructor feedback. Psychological safety serves as a theoretical lens for interpreting students’ contributions, responses to feedback, and continued engagement after lower early formative scores; the empirical analysis rests entirely on observable assessment, participation, and feedback data.Classroom interaction data make some aspects of participation visible, but cannot, by themselves, reveal the learning conditions students experienced. We therefore treat behavioural and linguistic traces as descriptive features of instructional interaction. In-class verbal assessments were more strongly associated with integrative performance than group-based tasks, and students who maintained or increased participation following lower early scores showed upward formative trajectories. Instructor feedback was consistently characterised by high clout, positive tone, and analytic clarity, which we interpret as text-level features of the feedback environment. Grounded in educational psychology and learning analytics, the study offers a practical way to examine how assessment, participation, and feedback align with active learning instruction, using descriptive correlations, bootstrap confidence intervals, within-family false discovery rate adjustments, and an exploratory Classification and Regression Tree (CART) visualisation. The findings are specific to a single-course, small-cohort case based on one instructor’s assessments and synchronous records.
Situated against the backdrop of an evolving regulatory landscape, this study investigates the perceptions of United Kingdom university educators regarding the use of generative artificial intelligence in summative assessment. Through a mixed-methods survey of 330 academic staff, the research identifies a significant gap between high levels of digital literacy and low evaluative trust. While respondents report high familiarity with AI tools, confidence in the ability of artificial intelligence to provide fair or nuanced marks remains low. Qualitative analysis of open text questions reveals a nuance crisis, where educators perceive these systems as incapable of comprehending complex student submissions. The findings reveal significant gender disparities in technical proficiency, however, a consistent pattern of low evaluative trust persists across teaching experience levels, with no statistically significant differences detected based on professional seniority, suggesting a potential need to consider alternative pedagogical frameworks, such as performative and synchronous assessments, to safeguard academic integrity.
We present high-frequency surface water measurements and discrete depth profiles of nitrous oxide (N2O) and methane (CH4) across the eastern Canadian Arctic Ocean, during early autumn 2023. Over the entire study region, surface water N2O concentrations were close to atmospheric saturation (96–111%), while surface water CH4 was generally supersaturated (between 197 and 445%). The highest surface water saturation of both gases was observed in northern Foxe Basin, a region of intense mixing over a shallow continental shelf. To examine the influence of vertical mixing on N2O and CH4 distributions, we coupled our field observations with output from a numerical model of the Arctic Ocean. This analysis revealed that gas supersaturation was associated with regions of enhanced vertical mixing, as indicated by elevated vertical eddy diffusivity (Kz) and weakly stratified seawater density profiles. Our results also suggest a significant imprint of lateral advection on the surface water distribution of N2O and CH4. Air–sea fluxes of N2O and CH4 during our early autumn sampling period exceeded previous summertime estimates from the eastern Canadian Arctic by up to an order of magnitude. On the basis of this observation, and an analysis of seasonal variability in model-derived mixing intensity, we argue that summertime studies may not adequately resolve high air–sea fluxes during early autumn associated with reduced stratification, low sea-ice cover, elevated mixing and wind-driven gas exchange. Our work highlights potential variability in surface gas concentrations associated with enhanced water column stratification under warming conditions across the Arctic Ocean and prompts the need for further measurements over a greater part of the seasonal cycle.
