English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

This paper presents an evaluation of several approaches of plants speciesdistribution modeling based on spatial, environmental and co-occurrences datausing machine learning methods. In particular, we re-evaluate the environmentalconvolutional neural network model that obtained the best performance of theGeoLifeCLEF 2018 challenge but on a revised dataset that fixes some of theissues of the previous one. We also go deeper in the analysis of co-occurrencesinformation by evaluating a new model that jointly takes environmentalvariables and co-occurrences as inputs of an end-to-end network. Results showthat the environmental models are the best performing methods and that there isa significant amount of complementary information between co-occurrences andenvironment. Indeed, the model learned on both inputs allows a significantperformance gain compared to the environmental model alone.

Evaluation of Deep Species Distribution Models using Environment and Co-occurrences