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Model . 2024
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Model . 2024
License: CC BY
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ZENODO
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License: CC BY
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DifferLand: A differentiable hybrid machine learning framework for retrieving unknown functional forms in terrestrial biosphere models

appsOther research productkeyboard_double_arrow_right Model 27 Aug 2024Publisher:Zenodo
Authors: Fang, Jianing; Gentine, Pierre;

doi: 10.5281/zenodo.13380471 , 10.5281/zenodo.14009133 , 10.5281/zenodo.13380472

DifferLand: A differentiable hybrid machine learning framework for retrieving unknown functional forms in terrestrial biosphere models

Abstract

This repository contains the code and data accompanying the manuscript "Exploring Optimal Complexity for Water Stress Representation in Terrestrial Carbon Models: A Hybrid-Machine Learning Model Approach,"to be published soon by JAMES. Please refer to the README.md file for detail usage instructions. The calibrated parameters from the runs used in the manuscript can be found in the DifferLand_JAMES_calibrated_parameters.zip file.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
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Average