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◆ Preventive veterinary medicine2026-09-10

Decoding persistent elevated mortality in farmed marine Scottish Atlantic salmon (Salmo salar L.) using artificial intelligence and logical modelling.

Meadhbh Moriarty, Sonia J Duguid, Sarah-Jane Parker, Joanne M Murphy, Ronald J Smith, Neil L Purvis, Alexander G Murray

原始摘要(英文原文)· Original abstract
Atlantic salmon (Salmo salar) farming, an important contributor to global seafood production, is the dominant sector of Scottish aquaculture. High mortality has been experienced in recent years, reflecting complex interactions between environment, pathogens, and operational factors. Understanding mortality trends enables proactive and empowered management and sustainable production. We developed a framework combining artificial intelligence (AI)-assisted data preparation, logic-based modelling and expert validation to identify and interpret mortality patterns from publicly available datasets. Definitions of recurrent mortality, persistent mortality and elevated mortality were developed to support reproducible and transparent interpretation. The framework was applied to Scottish marine salmon production data to identify sites exhibiting elevated mortality and to prioritise case for further investigation. Applying predefined criteria for recurrent, persistent and elevated mortality to 216 sites, 188 sites showed no evidence of persistent elevated mortality, 25 sites were prioritised for expert analysis with 20 sites taken forward for stakeholder engagement. Further evaluation involving additional data determined that 10 sites experienced persistent elevated mortality with the same cause, the remaining 10 represented recurrent elevated mortality arising from different factors. Expert review confirmed that prioritising ambiguous cases improved sensitivity at the expense of specificity, hence a relatively high false positive rate is needed to make assessment safely. This outcome demonstrated the value of combining automated screening with expert interpretation when assessing complex health and production datasets. Beyond aquaculture, the framework may provide a transferable approach for analysing mortality patterns in other production systems characterised by repeated production cycles and routinely collected mortality data.
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Decoding persistent elevated mortality in farmed marine Scottish Atlantic salmon (Salmo salar L.) using artificial intelligence and logical modelling. — 科研速览 Science Skim