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  <rdf:Description rdf:about="https://doi.org/10.5281/zenodo.11421746">
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    <dc:description>Project Overview:  This is the first release of our Bayesian-based fire models, designed for fire prediction and analysis using Bayesian inference and simple fire models. The release here is the base code and information used in the 'State of Wildfire's report 2023/24'. https://doi.org/10.5194/essd-2024-218  Key Features:    ConFire fire model now implemented with zero-inflated logistic link distribution  Configuration files for near real-time, attribution and future projections for Greece, Canada, and NW Amazon.  Utilizes various environmental and climatic data for isimip and Copernicus data store  Robust statistical analysis now uses PyMC at version 5 and ArviZ.   Installation and Usage:  For detailed installation and usage instructions, please refer to the README, also in this repository archive.  Acknowledgments:  Special thanks to all contributors and the developers of the dependencies used in this project. Particularly Maria Lucia Ferreira Barbosa,  Douglas Kelley, Chantelle Burton  Full Changelog: https://github.com/douglask3/Bayesian_fire_models/compare/v0.1...SoW23_v0.1</dc:description>
    <dc:subject>Canada</dc:subject>
    <dc:subject>Attribution</dc:subject>
    <dc:subject>Greece</dc:subject>
    <dc:subject>Amazonia</dc:subject>
    <dc:subject>Wildfire</dc:subject>
    <dc:subject>Climatic changes</dc:subject>
    <dc:subject>Fire</dc:subject>
    <dc:subject>Bayesian statistics</dc:subject>
    <dc:subject>Future projections</dc:subject>
    <dc:creator>Barbosa, Maria Lucia Ferreira, Kelley, Douglas, Burton, Chantelle, Anderson, Liana, </dc:creator>
    <dc:date>2024-06-03</dc:date>
    <dct:abstract>Project Overview:  This is the first release of our Bayesian-based fire models, designed for fire prediction and analysis using Bayesian inference and simple fire models. The release here is the base code and information used in the 'State of Wildfire's report 2023/24'. https://doi.org/10.5194/essd-2024-218  Key Features:    ConFire fire model now implemented with zero-inflated logistic link distribution  Configuration files for near real-time, attribution and future projections for Greece, Canada, and NW Amazon.  Utilizes various environmental and climatic data for isimip and Copernicus data store  Robust statistical analysis now uses PyMC at version 5 and ArviZ.   Installation and Usage:  For detailed installation and usage instructions, please refer to the README, also in this repository archive.  Acknowledgments:  Special thanks to all contributors and the developers of the dependencies used in this project. Particularly Maria Lucia Ferreira Barbosa,  Douglas Kelley, Chantelle Burton  Full Changelog: https://github.com/douglask3/Bayesian_fire_models/compare/v0.1...SoW23_v0.1</dct:abstract>
    <dc:title>ConFire: State of Wildfires 2023/24</dc:title>
    <dc:identifier>10.5281/zenodo.11421746</dc:identifier>
    <dc:type>software</dc:type>
    <dct:references>https://doi.org/10.5281/zenodo.11421746</dct:references>
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