Welcome to the AI WitchLab
... where cutting-edge AI developments and real-world research and practice meet the playful spirits of cyber mischief. Enter a world where the seriousness of progress coexists under one roof with the snark digital devilry, keeping things irreverently real.
No Witchwork.
Millions of undetermined specimen vouchers need identification.
Millions of undetermined specimen vouchers in the global herbaria meet the lack of skilled scientific personnel.
However, this is a complex problem, yet no comfortable solution to automatically identify herbarium specimens due to:
- Lack of skilled scientific personnel to resolve the problem
- Possible species gaps due to yet unknown specimens (see: Hortal et al., 2015)
- Plant Functional Traits concept may support problem-solving, but it has not yet been explored regarding environmental plant traits
- Use of AI / deep learning / CNNs has not yet been developed to resolve the problem
Topography as a set of predictors in SDM.
This study considers topography as an overarching concept for different aspects that can serve as predictors in species distribution models.
- How can a particular key aspect pattern influence species distribution?
- How can a particular key aspect pattern in topography be measured in a GLMM?
