Latitudinal Patterns in Stabilizing Density Dependence of Forest Communities
Nature(2024)SCI 1区
Ecosystem Analysis and Simulation (EASI) Lab | Department of Biological Sciences | School of the Environment | Institute of Environmental Sciences | Department of Ecology | Forest Global Earth Observatory | Conservation Ecology Center | National Biobank of Thailand (NBT) | Thai Long Term Forest Ecological Research Project | Instituto Amazónico de Investigaciones Científicas Sinchi | Department of Plant Science | Department of Ecology and Evolutionary Biology | Departamento de Ciencias Forestales | Department of Science and Technology | University of Kisangani | Environmental Studies Department | Department of Forest Ecology | Cofrin Center for Biodiversity | Graduate School of Science | School of Forest | Global Earth Observatory (ForestGEO) | Department of Forest Management | Department of Wildland Resources | Environmental Change Institute | Sarawak Forest Department | Forest Research Institute Malaysia | Instituto de Investigación de Recursos Biológicos Alexander von Humboldt | Department of Biology | Department of Forest Biology | Department of Natural Resources and Environmental Studies | Department of Botany and Plant Pathology | UK Centre for Ecology & Hydrology | Department of Environmental Science | Theoretical Ecology
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
