MAE
MAE on World Data Ocean: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on mae in some way — the news, the analysis, the deep dives, and the occasional surprise find. A Global Hub for Ocean Intelligence 4 World Data Ocean is a centralized digital platform where researchers, scientists, and ocean enthusiasts converge to explore, analyze, and… New stories are added to this page as we find them, so check back if you want to keep up with what is happening around mae, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything World Data Ocean is covering right now.
MAELSTROM, a machine learning-based approach for stock assessment
Effective fisheries management demands a balance between resource utilization and ecological sustainability. Introducing Maelstrom, a novel, machine learning-based approach to stock assessment that addresses limitations of traditional models. This multispecies predictive model, built on neural networks, integrates fishery-dependent and -independent data to forecast stock abundance, even with shorter time series. Benchmarked against the established a4a framework, Maelstrom demonstrates comparable accuracy and highlights the value of considering ecological interactions.
Two parameter analytical framework for surface layer salinity assessment in highly stratified microtidal estuary
Seawater intrusion poses a significant threat to coastal resources, particularly within highly stratified microtidal estuaries where predictive tools are lacking. This study introduces a novel, two-parameter analytical framework for assessing surface layer salinity, calibrated and validated using extensive field data from the Neretva River estuary. Building on established models, this framework incorporates four key modifications, including a salinity indicator (kforc10) derived from observed seawater level. Demonstrating strong performance (R² = 0.83-0.94), this computationally efficient tool offers practical support for coastal management and informed decision-making.