AUTHOR=Mieruch Sebastian , Kreps Gastón , Chouai Mohamed , Reimers Felix , Vredenborg Myriel , Rabe Benjamin , Tippenhauer Sandra , Behrendt Axel 

TITLE=SalaciaML-2-Arctic — a deep learning quality control algorithm for Arctic Ocean temperature and salinity data

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1661208

DOI=10.3389/fmars.2025.1661208

ISSN=2296-7745

ABSTRACT=We have extended a classical quality control (QC) algorithm by integrating a deep learning neural network, resulting in SalaciaML-2-Arctic, a tool for automated QC of Arctic Ocean temperature and salinity profile data. The neural network component was trained on the Unified Database for Arctic and Subarctic Hydrography (UDASH), which has been quality-controlled and labeled by expert oceanographers. SalaciaML-2-Arctic successfully reproduces human expertise by correcting misclassifications made by the classical algorithm, reducing False Negatives (samples incorrectly classified as “bad”) by 96% for temperature and 99% for salinity. When used in combination with a visual post-QC by human experts, it achieves a workload reduction of approximately 60% for temperature and 85% for salinity. All code and data required to reproduce the analysis or apply the method to other datasets are openly available via PANGAEA and GitHub. Moreover, SalaciaML-2-Arctic is accessible as a browser-based application at https://mvre.autoqc.cloud.awi.de, enabling its use without software installation or programming knowledge.