The challenge focused on the data-driven predictability of global sea surface temperatures (SSTs), a key factor in climate forecasting, ecosystem management, fisheries management, and climate change monitoring. The challenge involved forecasting SST anomalies (SSTAs) three months in advance using historical data and included a special task of predicting SSTAs nine months ahead for the Baltic Sea. Participants utilized various machine learning approaches to tackle the task, leveraging data from ERA5. This paper discusses the methodologies employed, the results obtained, and the lessons learned, offering insights into the future of climate-related predictive modeling.

Underwater archeologists sponsored by the World Wide Fund for Nature (WWF) have found an Enigma machine at the bottom of the Baltic Sea, likely from a submarine that Germany scuttled at the end of World War II. The divers made the discovery while searching the sea bed using a sonar device for abandoned fishing nets that can be harmful for sea life. Enigma machines, created in Nazi Germany in the 1930s and 1940s, were used to encode military messages; these codes were finally broken by the experts assembled by the British at Bletchley Park, work which fueled the creation of modern computers. Earlier this year, a four-rotor M4 Enigma cipher machine sold at an auction for £347,250 ($437,955). It, however, was in pristine condition while the rusty, barnacle-covered one found in the Baltic Sea has been deformed by decades spent in salt water.

Divers recovered the device at the bottom of Gelting Bay, on Germany's northern coast, while working to remove abandoned fishing nets that threaten marine life. Designed shortly after WWI by the engineer Arthur Scherbius for commercial usage, the cipher engine was adopted by many national governments and militaries. The portable device is best-known for its use by the Axis powers to encode military commands, for safe transmission by radio, as part of their rapid'blitzkrieg' strategy. Enigma featured a number of wheels, which together formed an electric circuit that repeatedly scrambled an entered character -- and reconfigured after each letter. German military models -- made more complex through the addition of a plugboard, for added scrambling -- and their codebooks were highly sought by the allies.

The assignment of individual fish to its stock of origin is important for reliable stock assessment and fisheries management. Otolith shape is commonly used as the marker of distinct stocks in discrimination studies. Our literature review showed that the application and comparison of alternative statistical classifiers to discriminate fish stocks based on otolith shape is limited. Therefore, we compared the performance of two traditional and four machine learning classifiers based on Fourier analysis of otolith shape using selected stocks of Atlantic cod (Gadus morhua) in the southern Baltic and Atlantic herring (Clupea harengus) in the western Norwegian Sea, Skagerrak and the southern Baltic Sea. Our results showed that the stocks can be successfully discriminated based on their otolith shapes.

Wärtsilä's strong commitment to developing the technologies, the system reliability, and the essential designs to enable the viability of autonomous shipping is again emphasised through its participation in an important research programme. Together with other leading marine sector and information & communication technology (ICT) companies, Wärtsilä is supporting a project aimed at creating the world's first autonomous marine transport system. The initial focus will be on developing a fully autonomous system for the Baltic Sea by the year 2025, with cargo ships and freight transportation being the first pilot applications. The programme is being largely financed by Tekes, the Finnish Funding Agency for innovation and the ecosystem platform is being handled by DIMECC, a leading breakthrough oriented co-creation ecosystem provider. The main corporate investors in the programme, apart from Wärtsilä, are Rolls-Royce, Cargotec, Ericsson, Meyer Turku, and Tieto.