CSciBox: Artificial intelligence for age-depth modeling

Artificial intelligence (AI) provides major opportunities for scientific analysis. Automated reasoners can explore problem spaces quickly and alert practitioners to possibilities that they had not considered. As a case in point, we describe the CSciBox system. Working with data from a paleorecord, s...

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Main Authors: Bradley, Elizabeth, Nelson, T.H., de Vesine, Rassbach
Format: Text
Language:unknown
Published: CU Scholar 2018
Subjects:
ice core
Online Access:https://scholar.colorado.edu/csci_facpapers/20
https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1018&context=csci_facpapers
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spelling ftunicolboulder:oai:scholar.colorado.edu:csci_facpapers-1018 2023-05-15T16:38:54+02:00 CSciBox: Artificial intelligence for age-depth modeling Bradley, Elizabeth Nelson, T.H. de Vesine, Rassbach 2018-01-01T08:00:00Z application/pdf https://scholar.colorado.edu/csci_facpapers/20 https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1018&context=csci_facpapers unknown CU Scholar https://scholar.colorado.edu/csci_facpapers/20 https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1018&context=csci_facpapers Computer Science Faculty Contributions text 2018 ftunicolboulder 2019-05-17T23:29:28Z Artificial intelligence (AI) provides major opportunities for scientific analysis. Automated reasoners can explore problem spaces quickly and alert practitioners to possibilities that they had not considered. As a case in point, we describe the CSciBox system. Working with data from a paleorecord, such as 14C dates from a sediment core (Fig. 1a) or 18O values from an ice core, CSciBox produces a set of age-depth models, plus a description of how each one was built and an assessment of its quality. Text ice core University of Colorado, Boulder: CU Scholar
institution Open Polar
collection University of Colorado, Boulder: CU Scholar
op_collection_id ftunicolboulder
language unknown
description Artificial intelligence (AI) provides major opportunities for scientific analysis. Automated reasoners can explore problem spaces quickly and alert practitioners to possibilities that they had not considered. As a case in point, we describe the CSciBox system. Working with data from a paleorecord, such as 14C dates from a sediment core (Fig. 1a) or 18O values from an ice core, CSciBox produces a set of age-depth models, plus a description of how each one was built and an assessment of its quality.
format Text
author Bradley, Elizabeth
Nelson, T.H.
de Vesine, Rassbach
spellingShingle Bradley, Elizabeth
Nelson, T.H.
de Vesine, Rassbach
CSciBox: Artificial intelligence for age-depth modeling
author_facet Bradley, Elizabeth
Nelson, T.H.
de Vesine, Rassbach
author_sort Bradley, Elizabeth
title CSciBox: Artificial intelligence for age-depth modeling
title_short CSciBox: Artificial intelligence for age-depth modeling
title_full CSciBox: Artificial intelligence for age-depth modeling
title_fullStr CSciBox: Artificial intelligence for age-depth modeling
title_full_unstemmed CSciBox: Artificial intelligence for age-depth modeling
title_sort cscibox: artificial intelligence for age-depth modeling
publisher CU Scholar
publishDate 2018
url https://scholar.colorado.edu/csci_facpapers/20
https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1018&context=csci_facpapers
genre ice core
genre_facet ice core
op_source Computer Science Faculty Contributions
op_relation https://scholar.colorado.edu/csci_facpapers/20
https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1018&context=csci_facpapers
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