Sustaining Biodiversity And Ecosystem Services In The Hindu Kush Himalaya

Jianchu Xu,Ruchi Badola,Nakul Chettri,Ram P. Chaudhary,Robert Zomer, Bharat Pokhrel,Syed Ainul Hussain,Sunita Pradhan, Rebecca Pradhan,Pratikshya Kandel,Kabir Uddin,Pariva Dobriyal,Upma Manral,Amanat K. Gill,Uma Partap,Kamal Aryal,Sarala Khaling,Yan Zhaoli, S. P. Singh

HINDU KUSH HIMALAYA ASSESSMENT: MOUNTAINS, CLIMATE CHANGE, SUSTAINABILITY AND PEOPLE（2019）

Cited 85|Views11

Abstract

Mountains make up 24% of the world’s land area, are home to 20% of the world’s population, provide 60–80% of the world’s fresh water, and harbour 50% of the world’s biodiversity hotspots (well-established). The United Nations recognized the importance of mountain ecosystems, both for conserving biological diversity and for sustaining humanity, in Chap. 13 of Agenda 21. More generally, ecosystem diversity, species diversity, genetic diversity, and functional diversity all play key roles in the ecosystem services that benefit people and communities (well-established).

Translated text

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

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

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper