Matthieu de Castelbajac , Sandra Bringay , Arnaud Sallaberry , Maximilien Servajean , Clémence Epinoux , Juan Carlos Molinero , Delphine Bonnet
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引用次数: 0
Abstract
Citizen science records are a valuable source of marine biodiversity data, especially where standardized sampling campaigns are limited in spatial or temporal scope. However, such records often contain biases and errors and typically require expert validation before they can reliably support scientific research. Validating large volumes of citizen science data remains an important challenge. In this paper, we present a semi-automated validation framework that combines a deep learning classifier with conformal prediction to generate sets of plausible taxonomic labels at multiple ranks, while providing rigorous control over prediction confidence. Extensive evaluation was carried out using 25,000 jellyfish records, both with and without prior validation, as well as against 800 expert-validated entries. Our results show that the method frequently produces singleton prediction sets that can be accepted automatically, offering a high-confidence and scalable solution for validating marine citizen science data.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.