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Fieldwork, including work done at sea, is a key component of many geoscientists' careers. Recent studies have highlighted the pervasive harassment faced by women and LGBTQ+ people during fieldwork. However, transgender and gender diverse (TGD) scientists face obstacles which have not yet been thoroughly examined. We fill this gap by sharing our experiences as TGD people. We have experienced sexual harassment, misconduct, privacy issues, and legal and medical struggles as we conduct seagoing work. In this work, we provide recommendations for individuals, cruise leaders, and institutions for making seagoing work safer for our communities. 

Abstract Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as training data, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future steps towards an operational sensor placement recommendation system. Our work could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality.