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...
Main Authors: | , , |
---|---|
Format: | Text |
Language: | unknown |
Published: |
CU Scholar
2018
|
Subjects: | |
Online Access: | https://scholar.colorado.edu/csci_facpapers/20 https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1018&context=csci_facpapers |
id |
ftunicolboulder:oai:scholar.colorado.edu:csci_facpapers-1018 |
---|---|
record_format |
openpolar |
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 |
_version_ |
1766029260215025664 |