Artificial Intelligence

Neural network as tools to replace oceanic data deficiencies

The importance of the cycle and speciation of nitrate and its isotopes (δ15N) in the ocean does not have to be demonstrated anymore. In an attempt to overcome the difficulty to compare the results of N/δ15N cycle models to a sparse set of data, Rafter and co-workers propose an original approach, based on artificial intelligence […]

Dec / 16 / 2019

Artificial intelligence helps investigate the oceanic zinc cycle

What explains the hitherto mysterious correlation between zinc (Zn) and silicon, an element not involved in the Zn cycle? Roshan and co-workers (2018, see reference below) used an artificial neural network (ANN, a machine learning technique inspired by biological neural systems) to produce a global climatology of dissolved Zn concentration, the first such global climatology […]

Jan / 24 / 2019

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