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|---|
| _creationDate | Datetime | 2020-06-28 4:24:55 PM |
| _modificationDate | Datetime | 2024-10-08 9:24:47 PM |
| _pageID | Integer | 55,138 |
| _pageName | Page | Reference:Reference-010807 |
| _pageTitle | String | Reference-010807
|
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1 row is stored for this page
| Field | Field type | Allowed values | Value |
|---|
| Firstname | String | | Leonard, Lorne; |
| BibType | String | journalArticle · report · bookSection · webpage · conferencePaper · MISC · thesis · blogpost · preprint | journalArticle |
| Title | String | | Using machine learning models to predict and choose meshes reordered by graph algorithms to improve execution times for hydrological modeling |
| Editors | String | | |
| Year | Date | | 2019-09-01 |
| Journal | String | | Environmental Modelling & Software |
| Booktitle | String | | |
| Volume | String | | 119 |
| Pages | String | | 84–98 |
| URL | String | | https://linkinghub.elsevier.com/retrieve/pii/S136481521830673X |
| DOI | String | | 10.1016/j.envsoft.2019.03.023 |
| ISBN | String | | |
| Note | String | | Auto downloaded ref at: 2020-06-28 |
1 row is stored for this page
| Field | Field type | Value |
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| PublicationMultipleModelsCargo | String | PIHM |
1 row is stored for this page
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| CSDMSGroups | String | Terrestrial Working Group · Coastal Working Group · Marine Working Group · Education and Knowledge Transfer (EKT) Working Group · Cyberinformatics and Numerics Working Group · Hydrology Focus Research Group · Chesapeake Focus Research Group · Critical Zone Focus Research Group · Human Dimensions Focus Research Group · Geodynamics Focus Research Group · Ecosystem Dynamics Focus Research Group · Coastal Vulnerability Initiative · Continental Margin Initiative · Artificial Intelligence & Machine Learning Initiative · Modeling Platform Interoperability Initiative · River Network Modeling Initiative | |
| FeatureRef | String | Yes · No | No |
| PublicationClusterID | String | | 0 |
| MS_PublicationClusterID | String | | 2945798111 |
| Semantic_ID | String | | 88a4e6fa769ce41ddb41db8a69207636f03ee055 |
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