Modelling the longevity of complex living systems

Fuente: arXiv
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Autor principal: Žliobaitė, Indrė
Formato: Preprint
Publicado: 2024
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author Žliobaitė, Indrė
author_facet Žliobaitė, Indrė
contents This extended abstract was presented at the Nectar Track of ECML PKDD 2024 in Vilnius, Lithuania. The content supplements a recently published paper "Laws of Macroevolutionary Expansion" in the Proceedings of the National Academy of Sciences (PNAS).
format Preprint
id arxiv_https___arxiv_org_abs_2410_02838
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Modelling the longevity of complex living systems
Žliobaitė, Indrė
Populations and Evolution
Machine Learning
Quantitative Methods
Applications
This extended abstract was presented at the Nectar Track of ECML PKDD 2024 in Vilnius, Lithuania. The content supplements a recently published paper "Laws of Macroevolutionary Expansion" in the Proceedings of the National Academy of Sciences (PNAS).
title Modelling the longevity of complex living systems
topic Populations and Evolution
Machine Learning
Quantitative Methods
Applications
url https://arxiv.org/abs/2410.02838