Y. Baidai

Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices (DFADs) from commercial echosounder buoys data

L. Dagorn, D. Gaertner, M. Capello, 2020, Fisheries Research.

A standard processing framework for the location data of satellite-linked buoys on drifting fish aggregating devices

M. Grande, H. Murua, B. Orue, 2022, Aquatic Living Resources.

Tuna aggregation dynamics at Drifting Fish Aggregating Devices: a view through the eyes of commercial echosounder buoys

L. Dagorn, D. Gaertner, M. Capello, 2020, ICES Journal of Marine Science.

Soil Health Changes Over a 25-Year Chronosequence From Forest to Plantations in Rubber Tree (Hevea brasiliensis) Landscapes in Southern Côte d'Ivoire: Do Earthworms Play a Role?

G. Forkuor, J. Tondoh, K. Dimobe, 2019, Front. Environ. Sci..

Functional attributes: Compacting vs decompacting earthworms and influence on soil structure

J. Huising, J. Tondoh, A. Guéi, 2012 .

Lessons learnt from the first large-scale biodegradable FAD research experiment to mitigate drifting FADs impacts on the ecosystem

L. Dagorn, G. Moreno, J. Murua, 2023, Marine Policy.

Machine learning to detect bycatch risk: Novel application to echosounder buoys data in tuna purse seine fisheries

L. Dagorn, L. Mannocci, M. Capello, 2021 .