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AGU22 - two sessions of interest

SM
Shaunna Morrison
Mon, Jul 25, 2022 7:58 PM

Hello MSA,

The AGU22 abstract https://agu.confex.com/agu/fm22/prelim.cgi/Home/0deadline
is drawing near (Aug 3) and I would like to draw your attention to two
sessions of potential interest:

IN017. Knowledge graph, machine learning, and artificial intelligence in
geosciences

Session Description: It is a common understanding in artificial
intelligence that the integration of primary knowledge in machine learning
will help lead to a better understanding of the big data analytical
techniques. Taxonomies, ontologies, and knowledge graphs can improve the
explainability and mitigate the black-box nature of machine learning.
Recently, there were also applications of knowledge graphs together with
machine learning algorithms to improve the quality of data science
processes. For instance, in remote sensing image analysis, ontologies have
been applied to improve the classification results. More successes are also
appearing in other domains. This session invites all geoscientists working
on knowledge graphs, explainable artificial intelligence, knowledge-fused
machine learning, as well as applications to submit their work. We hope the
session can create a venue where scientists can share their ideas and
experiences and discuss future works to better integrate knowledge graph,
machine learning, and other artificial intelligence techniques in
geoscience data analytics.

B031. Investigating Microbial Metabolism in the Archean Eon
Session Description: Geochemical evidence indicates that life’s core
metabolic pathways evolved in the Archean eon, early in Earth’s history.
The core microbial metabolisms of the Archean set the foundation for global
biogeochemical cycles and complex eukaryotic life.  However, reconstructing
the environmental conditions from which Archean metabolisms evolved, the
primitive oxidoreductase proteins and other cellular machinery that
performed metabolic electron transfer processes, and ecological network
interactions of early microbial communities is a major challenge for
understanding the evolution of life on Earth and other planetary bodies.
In this session, we invite researchers to present their work on all aspects
related to investigating the evolution of life and metabolism in the
Archean eon.  This includes identifying geochemical evidence of metabolic
pathways or environmental conditions 4.0 to 2.5 billion years old, studying
microbial metabolism in primitive analogue environments that potentially
resemble conditions of Archean microbial ecosystems, laboratory studies
that attempt to replicate Archean environmental conditions, laboratory
studies to construct primitive enzymes/biomolecules or prebiotic reactions
that led to the origin of microbial metabolic pathways, or analytical
studies modeling these complex early environments and/or potential feedback
systems with early microbial metabolisms.

Abstract https://agu.confex.com/agu/fm22/prelim.cgi/Home/0 deadline: Aug
3

I hope to see many of you there!

Best,
Shaunna

*Shaunna M. Morrison *| Carnegie Research Scientist
[image:
https://dtm.carnegiescience.edu/sites/all/themes/DTM/images/CSEP_LABS_Logo_-Color-Horizontal.png]
5241 Broad Branch Rd. NW | Washington, DC 20015
smorrison@carnegiescience.edu
Cell: 478-737-5786 <%28478%29%20737-5786> | Office: 202-478-8983
<%28202%29%20478-8983>
http://www.geo.arizona.edu/%7Eshaunnamm/www.carnegiescience.edu/smmorrison

Hello MSA, The AGU22 abstract <https://agu.confex.com/agu/fm22/prelim.cgi/Home/0>deadline is drawing near (Aug 3) and I would like to draw your attention to two sessions of potential interest: *IN017. Knowledge graph, machine learning, and artificial intelligence in geosciences* Session Description: It is a common understanding in artificial intelligence that the integration of primary knowledge in machine learning will help lead to a better understanding of the big data analytical techniques. Taxonomies, ontologies, and knowledge graphs can improve the explainability and mitigate the black-box nature of machine learning. Recently, there were also applications of knowledge graphs together with machine learning algorithms to improve the quality of data science processes. For instance, in remote sensing image analysis, ontologies have been applied to improve the classification results. More successes are also appearing in other domains. This session invites all geoscientists working on knowledge graphs, explainable artificial intelligence, knowledge-fused machine learning, as well as applications to submit their work. We hope the session can create a venue where scientists can share their ideas and experiences and discuss future works to better integrate knowledge graph, machine learning, and other artificial intelligence techniques in geoscience data analytics. *B031. Investigating Microbial Metabolism in the Archean Eon* Session Description: Geochemical evidence indicates that life’s core metabolic pathways evolved in the Archean eon, early in Earth’s history. The core microbial metabolisms of the Archean set the foundation for global biogeochemical cycles and complex eukaryotic life. However, reconstructing the environmental conditions from which Archean metabolisms evolved, the primitive oxidoreductase proteins and other cellular machinery that performed metabolic electron transfer processes, and ecological network interactions of early microbial communities is a major challenge for understanding the evolution of life on Earth and other planetary bodies. In this session, we invite researchers to present their work on all aspects related to investigating the evolution of life and metabolism in the Archean eon. This includes identifying geochemical evidence of metabolic pathways or environmental conditions 4.0 to 2.5 billion years old, studying microbial metabolism in primitive analogue environments that potentially resemble conditions of Archean microbial ecosystems, laboratory studies that attempt to replicate Archean environmental conditions, laboratory studies to construct primitive enzymes/biomolecules or prebiotic reactions that led to the origin of microbial metabolic pathways, or analytical studies modeling these complex early environments and/or potential feedback systems with early microbial metabolisms. *Abstract <https://agu.confex.com/agu/fm22/prelim.cgi/Home/0> deadline: Aug 3* I hope to see many of you there! Best, Shaunna -- *Shaunna M. Morrison *| Carnegie Research Scientist [image: https://dtm.carnegiescience.edu/sites/all/themes/DTM/images/CSEP_LABS_Logo_-_Color_-Horizontal.png] 5241 Broad Branch Rd. NW | Washington, DC 20015 smorrison@carnegiescience.edu Cell: 478-737-5786 <%28478%29%20737-5786> | Office: 202-478-8983 <%28202%29%20478-8983> <http://www.geo.arizona.edu/%7Eshaunnamm/>www.carnegiescience.edu/smmorrison