Introduction: FIRElinks, a Community for Society and Science
Fire Hazards: Socio-economic and Regional Issues(2024)
Abstract
AbstractFIRElinks (CA18135) originated from many efforts by a group of researchers after submitting a proposal for a COST Action. During four years, the main aim has been to develop an EU-spanning network of scientists and practitioners involved in forest fire research and land management with backgrounds such as fire dynamics, fire risk management, fire effects on vegetation, fauna, soil and water, and socioeconomic, historical, geographical, political perception, and land management approaches. Communities from different scientific and geographic backgrounds allowing the discussion of different experiences and the emergence of new approaches to fire research were connected. Working group number 5 was developed to power synergistic collaborations between European research groups and stakeholders to synthesize the existing knowledge and expertise and to define a concerted research agenda which promotes an integrated approach to create fire-resilient landscapes from a regional and socioeconomic point of view, taking into account how to teach the population, stakeholders, and policymakers considering the biological, biochemical, and physical, but also socioeconomic, historical, geographical, sociological, perception, and policy constraints. In this edited book, the main conclusion of working group 5 was addressed considering different study cases and methods developed by recognized experts over Europe: there is an urgent societal need to manage wildfires due to the expected further intensification and geographical spreading of its regimes under global change.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
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
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined